{"generatedAt":"2026-09-17T19:43:02.963Z","passCount":115,"latestPass":115,"models":["ndd_panel_classifier_v1.joblib","ndd_panel_classifier_v2.joblib","ndd_panel_classifier_v3.joblib","ndd_panel_classifier_v4_no_ot_ablation.joblib","ndd_panel_classifier_v4_otkeywords.joblib","ndd_panel_classifier_v5_all6_augmented.joblib","ndd_panel_classifier_v6_all7_augmented.joblib","ndd_panel_classifier_v7_all10_augmented.joblib","ndd_panel_classifier_v8_all10_corrected.joblib","ndd_panel_classifier_v9_all10_corrected_mlp.joblib","ndd_panel_classifier_v10_all10_corrected_softvote.joblib","ndd_panel_classifier_v11_all10_corrected_calibrated_softvote.joblib","ndd_panel_classifier_v12_family_weighted_mlp.joblib","ndd_panel_classifier_v13_hard_control_aware.joblib","ndd_panel_classifier_v14_specificity_gate.joblib","ndd_panel_classifier_v15_neural_rescue_gate.joblib","ndd_panel_classifier_v16_evidence_gate.joblib","ndd_panel_classifier_v17_stacked_representation.joblib","ndd_panel_classifier_v18_hard_negative.joblib","ndd_panel_classifier_v19_set31_recall_rescue.joblib","ndd_panel_classifier_v20_set32_specificity_repair.joblib","ndd_panel_classifier_v21_set33_failure_repair.joblib","ndd_panel_classifier_v22_set34_residual_repair.joblib","ndd_panel_classifier_v23_set35_comparator_repair.joblib","ndd_panel_classifier_v24_source_feature_integration.joblib"],"latestModel":"ndd_panel_classifier_v24_source_feature_integration.joblib","sourceHealthTotal":36,"clinvarCount":4131,"geoCount":3152,"sraCount":6868,"openness":{"openTargetsDiseaseCount":1929,"reactomePathwayCount":12,"hpTermCount":19944,"mondoTermCount":31886},"trainingSnapshot":{"selectedModel":"source_feature_integration_stacked_mlp","holdoutSet":"set11","holdoutRows":16,"holdoutF1Thr038":0.9333333333333333,"holdoutRecallThr038":0.875,"holdoutSpecificityThr038":1},"externalValidation":{"cohort":"set41","cohortRows":144,"primaryModelThr038":"v24_sf","bestModelThr038":"v24_sf","summaryArtifactPath":"/repo/artifacts/downloads/training/set41_suite_summary_v1.json","primaryF1Thr038":0.993103448275862,"primaryRecallThr038":1,"primarySpecificityThr038":0.9861111111111112,"primaryFnGenesThr038":[],"v11F1Thr038":0.993103448275862,"v11RecallThr038":1,"v11SpecificityThr038":0.9861111111111112,"v11FnGenesThr038":[]},"modelGovernance":{"modelId":"v24_source_feature_integration","governanceStatus":"final_governance_signoff_complete_research_triage_ready","productionUseStatus":"approved_research_triage_blocked_clinical","candidateThreshold":0.71,"set41SurveillanceStatus":"set41_surveillance_passed","governancePackagePath":"/repo/artifacts/downloads/training/v24_model_governance_package_v1.json","reproducibilityManifestPath":"/repo/artifacts/downloads/training/v24_reproducibility_manifest_v1.json","primaryMonitoringGene":"MAP3K7"},"modelFinalization":{"modelId":"v24_source_feature_integration","finalSignoffStatus":"approved_for_research_triage_use","productionUseStatus":"approved_research_triage_blocked_clinical","clinicalUseStatus":"prohibited","candidateThreshold":0.71,"set41SurveillanceStatus":"set41_surveillance_passed","endpointReadinessStatus":"ready_for_research_triage_endpoint","manifestReplayStatus":"passed","allowedUse":["Research triage for prioritizing genes for expert review.","Internal experiment planning and residual monitoring.","Batch candidate scoring through the governed v24 research scorer."],"prohibitedUse":["Clinical diagnosis or treatment decisions.","Standalone pathogenicity, penetrance, or disease-causality claims.","Patient-specific interpretation or medical decision support."],"signoffArtifactPath":"/repo/artifacts/downloads/training/v24_final_governance_signoff_v1.json","finalManifestArtifactPath":"/repo/artifacts/downloads/training/v24_final_reproducibility_manifest_v1.json","manifestReplayArtifactPath":"/repo/artifacts/downloads/training/v24_manifest_replay_pass99_v1.json","endpointReadinessContractPath":"/repo/artifacts/downloads/training/v24_endpoint_readiness_contract_v1.json","scoringCommandTemplate":".venv-training/bin/python scripts/python/score_v24_research_genes_v1.py --threshold 0.71 GENE_SYMBOL"},"clinicalReadiness":{"modelId":"v24_source_feature_integration","modelTrainingStatus":"trained_successfully","researchTriageStatus":"ready_for_governed_research_triage","clinicalReadinessStatus":"blocked_pending_clinical_validation","diseaseDetectionStatus":"not_ready_for_clinical_disease_detection","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"blockingGateCount":7,"candidateThreshold":0.71,"readinessSummary":"v24 is trained successfully and ready for governed research triage, but patient-level validation missing or incomplete; prospective validation readiness exists through Pass111 but real prospective evidence is not complete. Therefore it is not ready for clinical disease detection, diagnosis, treatment decisions, or patient-specific interpretation.","blockingGates":["disease_specific_endpoint_definition","retrospective_patient_level_validation","prospective_multisite_validation","subgroup_bias_and_calibration","clinical_risk_management_qms","regulatory_pathway_review","postmarket_monitoring_pccp"],"nextActions":["Acquire an IRB/DUA-appropriate deidentified patient-level clinical cohort with clinician-adjudicated disease/phenotype endpoints.","Lock the intended-use statement, disease-specific endpoint, inclusion/exclusion criteria, and analysis plan before touching patient-level validation data.","Run retrospective patient-level validation of the frozen v24 scorer at threshold 0.71, including sensitivity, specificity, PPV, NPV, calibration, and decision-curve analysis.","Run subgroup bias and calibration checks by site, assay, ancestry, age, sex, and phenotype severity before any clinical workflow claim.","Pre-register and run a prospective multisite validation workflow before any disease-detection, diagnosis, or treatment-support use.","Use the Pass111 protocol and event template only after Pass110 passes on real deidentified cohort evidence.","Prepare clinical risk management, quality-system traceability, monitoring, rollback, and AI change-control documentation before regulatory review."],"readinessArtifactPath":"/repo/artifacts/downloads/training/pass100_clinical_translation_readiness_v1.json"},"patientValidationReadiness":{"modelId":"v24_source_feature_integration","modelTrainingStatus":"trained_successfully","researchTriageStatus":"ready_for_governed_research_triage","patientValidationStatus":"ready_pending_real_adjudicated_cohort","clinicalReadinessStatus":"blocked_pending_patient_level_validation","diseaseDetectionStatus":"not_ready_for_clinical_disease_detection","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"candidateThreshold":0.71,"preparedGateCount":4,"remainingBlockingGateCount":6,"readinessSummary":"Pass101 locks the intended-use endpoint, schema, cohort CSV template, and patient-level validation runner; v24 remains trained and research-triage ready, but clinical use is still prohibited until a real adjudicated patient-level cohort passes validation.","remainingBlockingGates":["retrospective_patient_level_validation","prospective_multisite_validation","subgroup_bias_and_calibration","clinical_risk_management_qms","regulatory_pathway_review","postmarket_monitoring_pccp"],"nextActions":["Secure an IRB/DUA-appropriate deidentified retrospective cohort that meets the Pass101 schema and minimum cohort requirements.","Run validate_pass101_patient_cohort_v1.py on the cohort before scoring and reject any PHI-bearing or undersized file.","Freeze the row-level cohort manifest, score candidate_gene_symbol values with the governed v24 threshold 0.71, and compute the locked primary and secondary metrics.","Review false negatives, false positives, calibration, and subgroup strata before deciding whether a clinical validation pass can advance.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective clinical gates pass."],"protocolPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_protocol_v1.json","schemaPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_schema_v1.json","cohortTemplatePath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_cohort_template_v1.csv","templateCheckPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_template_check_v1.json","readinessArtifactPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_readiness_v1.json"},"retrospectiveValidation":{"modelId":"v24_source_feature_integration","retrospectiveValidationStatus":"dry_run_sample_not_clinical_evidence","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"eligibleForClinicalValidation":false,"candidateThreshold":0.71,"rowsScored":4,"scoreJoinStatus":"complete","evidenceBoundary":"Dry-run sample/template output is not clinical evidence and cannot support disease-detection claims.","dryRunConfusion":{"tp":1,"fp":1,"tn":1,"fn":1},"nextActions":["Replace the sample cohort and score template with a real DUA/IRB-appropriate deidentified cohort.","Run Pass101 cohort validation before scoring and reject PHI-bearing or undersized files.","Score the frozen v24 model at threshold 0.71 without threshold tuning from patient outcomes.","Review residuals, calibration, subgroup strata, and prospective-study requirements before clinical claims."],"scoreTemplatePath":"/repo/artifacts/downloads/training/pass102_patient_validation_score_template_v1.csv","dryRunPath":"/repo/artifacts/downloads/training/pass102_retrospective_validation_dry_run_v1.json","cohortTemplatePath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_cohort_template_v1.csv"},"externalValidationIntake":{"modelId":"v24_source_feature_integration","intakeStatus":"dry_run_sample_not_clinical_evidence","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"readyForPatientLevelValidationExecution":false,"candidateThreshold":0.71,"candidateScoreInputRows":4,"cohortValidationStatus":"schema_passed_sample_not_clinical_evidence","lockedValidationStatus":"dry_run_sample_not_clinical_evidence","scoreJoinStatus":"complete","rowsScored":4,"lockedValidationConfusion":{"tp":1,"fp":1,"tn":1,"fn":1},"nextActions":["Replace the sample/template cohort with a real DUA/IRB-appropriate deidentified cohort.","Run Pass103 intake without template mode and reject any cohort that fails Pass101 schema, PHI, or minimum-size gates.","Score candidate_gene_symbol values with frozen v24 at threshold 0.71 and keep score rows keyed by patient_uid plus candidate_gene_symbol.","Run the Pass102 locked retrospective validator on the frozen package before any prospective clinical workflow study.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective gates pass."],"intakePackagePath":"/repo/artifacts/downloads/training/pass103_external_validation_intake_package_v1.json","candidateScoreInputPath":"/repo/artifacts/downloads/training/pass103_external_validation_candidate_score_input_v1.csv","cohortCsvPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_cohort_template_v1.csv","scoreCsvPath":"/repo/artifacts/downloads/training/pass102_patient_validation_score_template_v1.csv"},"retrospectiveAcceptance":{"modelId":"v24_source_feature_integration","acceptanceGateStatus":"blocked_sample_not_clinical_evidence","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"retrospectiveGatePassed":false,"readyForProspectiveValidation":false,"candidateThreshold":0.71,"blockingGateCount":5,"blockingGates":["real_cohort_eligibility","primary_metrics","calibration","subgroup_reportability","prospective_validation"],"observedMetrics":{"n":4,"sensitivity":0.5,"specificity":0.5,"ppv":0.5,"npv":0.5,"auroc":0.75,"brierScore":0.195775},"nextActions":["Run Pass104 on a real non-template Pass103 package after the cohort passes Pass101 eligibility.","Require locked sensitivity, specificity, NPV, PPV, AUROC, Brier score, and subgroup reportability gates before prospective workflow planning.","Complete expert review of false negatives and false positives before any clinical validation decision.","Run a prospective multisite validation workflow before any clinical disease-detection, diagnosis, or treatment-support claim."],"policyPath":"/repo/artifacts/downloads/training/pass104_retrospective_acceptance_gate_policy_v1.json","dryRunPath":"/repo/artifacts/downloads/training/pass104_retrospective_acceptance_gate_dry_run_v1.json"},"cohortAdmissibility":{"modelId":"v24_source_feature_integration","admissibilityStatus":"blocked_sample_not_clinical_evidence","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"eligibleForRetrospectiveEvidence":false,"blockingGateCount":3,"blockingGates":["template_fingerprint","source_manifest","schema_and_minimums"],"cohortDiversity":{"uniqueSites":2,"positiveRows":2,"negativeRows":2},"phiValueScreen":{"checkedRows":4,"phiLikeValueCount":0},"nextActions":["Provide a real DUA/IRB-appropriate deidentified cohort plus source manifest before Pass102/104 evidence use.","Reject any cohort with template fingerprints, PHI-like values, duplicate patient-gene candidates, missing governance metadata, or Pass101 minimum failures.","Only after Pass105 and Pass101 pass should the frozen v24 score join and locked retrospective acceptance gates be run.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective gates pass."],"dryRunPath":"/repo/artifacts/downloads/training/pass105_cohort_admissibility_dry_run_v1.json","cohortCsvPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_cohort_template_v1.csv","sourceManifestJsonPath":null},"sourceManifestReadiness":{"modelId":"v24_source_feature_integration","sourceManifestStatus":"blocked_template_source_manifest","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"eligibleForCohortAdmissibility":false,"blockingGateCount":1,"blockingGates":["template_placeholders"],"manifestSummary":{"cohortId":"TEMPLATE_COHORT_ID_REPLACE_ME","deidentificationMethod":"hipaa_safe_harbor","provenanceSiteCount":2},"nextActions":["Replace template values with real governance metadata from the cohort owner.","Keep permitted use limited to governed research-triage validation.","Run Pass106 before Pass105 so source-manifest gaps are resolved before cohort admissibility.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective gates pass."],"schemaPath":"/repo/artifacts/downloads/training/pass106_source_manifest_schema_v1.json","templatePath":"/repo/artifacts/downloads/training/pass106_source_manifest_template_v1.json","templateCheckPath":"/repo/artifacts/downloads/training/pass106_source_manifest_template_check_v1.json"},"sourceManifestCompletion":{"modelId":"v24_source_feature_integration","completionStatus":"blocked_missing_owner_evidence","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"readyForPass105Admissibility":false,"blockingItemCount":9,"blockingItems":[{"field":"cohort_id","currentStatus":"template_value","ownerRole":"data_steward"},{"field":"cohort_owner","currentStatus":"template_value","ownerRole":"institutional_sponsor"},{"field":"data_use_authorization","currentStatus":"template_value","ownerRole":"governance_officer"},{"field":"irb_or_ethics_approval","currentStatus":"template_value","ownerRole":"irb_or_ethics_contact_role"},{"field":"deidentification_method","currentStatus":"missing_owner_evidence","ownerRole":"privacy_officer"},{"field":"provenance_sites","currentStatus":"template_value","ownerRole":"cohort_operations_lead"},{"field":"adjudication_procedure","currentStatus":"template_value","ownerRole":"clinical_adjudication_lead"},{"field":"training_overlap_attestation","currentStatus":"template_value","ownerRole":"model_governance_steward"},{"field":"contact_role","currentStatus":"template_value","ownerRole":"data_steward_role"}],"upstreamBlockingGates":["schema_and_minimums","source_manifest","template_fingerprint","template_placeholders"],"nextActions":["Ask the cohort owner to replace all template_value and missing_owner_evidence fields in the Pass106 manifest template.","Keep all repository manifests role-based and deidentified; do not add names, emails, phone numbers, MRNs, exact dates, or free text PHI.","Rerun Pass106 and Pass105 before any score join, retrospective metric calculation, or acceptance-gate review.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective validation gates pass."],"completionChecklistPath":"/repo/artifacts/downloads/training/pass107_source_manifest_completion_checklist_v1.json","completionPacketPath":"/repo/artifacts/downloads/training/pass107_source_manifest_completion_packet_v1.json"},"cohortIntakePreflight":{"modelId":"v24_source_feature_integration","preflightStatus":"blocked_template_inputs_pending_real_cohort","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"readyForPass103Intake":false,"readyForPass102Validation":false,"blockingGateCount":4,"blockingGates":["schema_and_minimums","source_manifest","template_fingerprint","template_placeholders"],"cohortValidationStatus":"schema_passed_sample_not_clinical_evidence","sourceManifestStatus":"blocked_template_source_manifest","admissibilityStatus":"blocked_sample_not_clinical_evidence","commandPlan":{"pass106_validate_manifest":".venv-training/bin/python scripts/python/validate_pass106_source_manifest_v1.py --manifest-json <COMPLETED_SOURCE_MANIFEST_JSON> --schema-json artifacts/downloads/training/pass106_source_manifest_schema_v1.json --out-json artifacts/downloads/training/real_source_manifest_check_v1.json","pass105_admissibility":".venv-training/bin/python scripts/python/audit_pass105_cohort_admissibility_v1.py --cohort-csv <REAL_DEIDENTIFIED_COHORT_CSV> --source-manifest-json <COMPLETED_SOURCE_MANIFEST_JSON> --protocol-json artifacts/downloads/training/pass101_patient_level_validation_protocol_v1.json --schema-json artifacts/downloads/training/pass101_patient_level_validation_schema_v1.json --out-json artifacts/downloads/training/real_cohort_admissibility_v1.json --suite-summary-json artifacts/downloads/training/real_cohort_admissibility_summary_v1.json","pass103_intake":".venv-training/bin/python scripts/python/run_pass103_external_validation_intake_v1.py --cohort-csv <REAL_DEIDENTIFIED_COHORT_CSV> --protocol-json artifacts/downloads/training/pass101_patient_level_validation_protocol_v1.json --schema-json artifacts/downloads/training/pass101_patient_level_validation_schema_v1.json --candidate-score-input-csv artifacts/downloads/training/real_candidate_score_input_v1.csv --out-json artifacts/downloads/training/real_external_validation_intake_package_v1.json --suite-summary-json artifacts/downloads/training/real_external_validation_suite_summary_v1.json","pass102_validation":".venv-training/bin/python scripts/python/run_pass102_retrospective_validation_v1.py --cohort-csv <REAL_DEIDENTIFIED_COHORT_CSV> --score-csv <LOCKED_V24_SCORE_CSV> --protocol-json artifacts/downloads/training/pass101_patient_level_validation_protocol_v1.json --schema-json artifacts/downloads/training/pass101_patient_level_validation_schema_v1.json --out-json artifacts/downloads/training/real_retrospective_validation_v1.json","pass104_acceptance":".venv-training/bin/python scripts/python/evaluate_pass104_retrospective_acceptance_gates_v1.py --validation-json artifacts/downloads/training/real_retrospective_validation_v1.json --intake-json artifacts/downloads/training/real_external_validation_intake_package_v1.json --out-json artifacts/downloads/training/real_retrospective_acceptance_v1.json --suite-summary-json artifacts/downloads/training/real_retrospective_acceptance_summary_v1.json","preflight_inputs_observed":"observed_cohort_csv=artifacts/downloads/training/pass101_patient_level_validation_cohort_template_v1.csv observed_source_manifest_json=artifacts/downloads/training/pass106_source_manifest_template_v1.json"},"nextActions":["Replace the template source manifest and sample cohort CSV with real owner-governed deidentified files.","Run Pass108 preflight before generating candidate score inputs or locked v24 score joins.","Proceed to Pass103 only after Pass106, Pass105, and Pass101 gates clear without template mode.","Proceed to Pass102 only after frozen v24 score rows are produced for every patient_uid plus candidate_gene_symbol pair.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective validation gates pass."],"dryRunPath":"/repo/artifacts/downloads/training/pass108_cohort_intake_preflight_dry_run_v1.json","cohortCsvPath":"/repo/artifacts/downloads/training/pass101_patient_level_validation_cohort_template_v1.csv","sourceManifestJsonPath":"/repo/artifacts/downloads/training/pass106_source_manifest_template_v1.json"},"frozenScoringHandoff":{"modelId":"v24_source_feature_integration","scoringHandoffStatus":"blocked_preflight_not_ready_for_locked_scoring","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"readyForLockedV24Scoring":false,"readyForPass102Validation":false,"candidateThreshold":0.71,"lockedScoreRowCount":0,"requiredScoreOutputColumns":["patient_uid","candidate_gene_symbol","model_id","score_v24_sf","score_source"],"missingScoreGeneSymbols":[],"candidateScoreInput":{"path":"/repo/artifacts/downloads/training/pass103_external_validation_candidate_score_input_v1.csv","rowCount":4,"uniqueGeneCount":4,"templateOrSampleRowsDetected":true},"upstreamPreflight":{"preflightStatus":"blocked_template_inputs_pending_real_cohort","readyForPass103Intake":false,"clinicalUseStatus":"prohibited"},"commandPlan":{"run_pass103_intake":".venv-training/bin/python scripts/python/run_pass103_external_validation_intake_v1.py --cohort-csv <REAL_DEIDENTIFIED_COHORT_CSV> --candidate-score-input-csv artifacts/downloads/training/real_candidate_score_input_v1.csv --out-json artifacts/downloads/training/real_external_validation_intake_package_v1.json --suite-summary-json artifacts/downloads/training/real_external_validation_suite_summary_v1.json","score_genes_with_governed_v24":".venv-training/bin/python scripts/python/score_v24_research_genes_v1.py --threshold 0.71 --out artifacts/downloads/training/real_v24_gene_scores_v1.json <UNIQUE_CANDIDATE_GENE_SYMBOLS_FROM_PASS103>","assemble_locked_score_csv":".venv-training/bin/python scripts/python/generate_pass109_frozen_v24_scoring_handoff_v1.py --preflight-json artifacts/downloads/training/real_cohort_intake_preflight_v1.json --candidate-score-input-csv artifacts/downloads/training/real_candidate_score_input_v1.csv --scored-genes-json artifacts/downloads/training/real_v24_gene_scores_v1.json --locked-score-csv artifacts/downloads/training/real_locked_v24_scores_v1.csv --out-json artifacts/downloads/training/real_frozen_v24_scoring_handoff_v1.json","run_pass102_validation":".venv-training/bin/python scripts/python/run_pass102_retrospective_validation_v1.py --cohort-csv <REAL_DEIDENTIFIED_COHORT_CSV> --score-csv artifacts/downloads/training/real_locked_v24_scores_v1.csv --out-json artifacts/downloads/training/real_retrospective_validation_v1.json","run_pass104_acceptance":".venv-training/bin/python scripts/python/evaluate_pass104_retrospective_acceptance_gates_v1.py --validation-json artifacts/downloads/training/real_retrospective_validation_v1.json --intake-json artifacts/downloads/training/real_external_validation_intake_package_v1.json --out-json artifacts/downloads/training/real_retrospective_acceptance_v1.json --suite-summary-json artifacts/downloads/training/real_retrospective_acceptance_summary_v1.json"},"nextActions":["Pass109 frozen v24 scoring handoff: complete Pass108 with real source-manifest and cohort files before scoring.","Run Pass103 to produce real candidate score input rows, then score the unique candidate_gene_symbol values through the governed v24 scorer at threshold 0.71.","Assemble the locked score CSV with patient_uid, candidate_gene_symbol, model_id, score_v24_sf, and score_source before Pass102.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until retrospective and prospective validation gates pass."],"handoffPath":"/repo/artifacts/downloads/training/pass109_frozen_v24_scoring_handoff_v1.json","scoreSchemaPath":"/repo/artifacts/downloads/training/pass109_frozen_v24_score_schema_v1.json","scoreTemplatePath":"/repo/artifacts/downloads/training/pass109_frozen_v24_score_template_v1.csv","lockedScoreCsvPath":null},"retrospectiveValidationLaunch":{"modelId":"v24_source_feature_integration","executionStatus":"blocked_preflight_not_ready_for_retrospective_execution","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"readyForRetrospectiveExecution":false,"retrospectiveGatePassed":false,"readyForProspectiveValidation":false,"readyForClinicalUse":false,"candidateThreshold":0.71,"stageStatuses":{"pass108_preflight":"blocked_template_inputs_pending_real_cohort","pass103_intake":"not_run","pass109_scoring_handoff":"not_run","pass102_validation":"not_run","pass103_final_intake":"not_run","pass104_acceptance":"not_run"},"stageMetrics":{"candidateScoreInputRows":0,"lockedScoreRowCount":0,"rowsScored":0,"blockingGateCount":4},"nextActions":["Pass110 retrospective validation launch: replace template inputs with real source-manifest, cohort, and governed v24 scored-gene artifacts.","Execute the launcher only after Pass108 clears a non-template cohort and source manifest.","Use the Pass110 stage artifacts to review Pass102 residuals, Pass104 acceptance gates, and prospective-validation readiness.","Keep clinical disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims prohibited until prospective validation and governance gates pass."],"launchPath":"/repo/artifacts/downloads/training/pass110_retrospective_validation_launch_dry_run_v1.json","suiteSummaryPath":"/repo/artifacts/downloads/training/pass110_retrospective_validation_launch_suite_summary_v1.json","preflightPath":"/repo/artifacts/downloads/training/pass110_retrospective_validation_launch_pass108_preflight_v1.json","validationPath":"/repo/artifacts/downloads/training/pass110_retrospective_validation_launch_pass102_validation_v1.json","acceptancePath":"/repo/artifacts/downloads/training/pass110_retrospective_validation_launch_pass104_acceptance_v1.json"},"prospectiveValidationReadiness":{"modelId":"v24_source_feature_integration","prospectiveValidationStatus":"blocked_retrospective_launch_not_ready_for_prospective_validation","clinicalUseStatus":"prohibited","canClaimDiseaseDetection":false,"readyForProspectiveEnrollment":false,"prospectiveValidationGatePassed":false,"readyForQmsRegulatoryReview":false,"readyForClinicalUse":false,"candidateThreshold":0.71,"eventLogCheck":{"rowCount":0,"positiveCount":0,"negativeCount":0,"uniqueSiteCount":0,"templateOrSampleRowsDetected":true,"schemaStatus":"passed","minimumsPassed":false},"prospectiveMetrics":{"n":0,"sensitivity":null,"specificity":null,"ppv":null,"npv":null,"auroc":null,"brierScore":null},"upstreamRetrospectiveLaunch":{"executionStatus":"blocked_preflight_not_ready_for_retrospective_execution","retrospectiveGatePassed":false,"readyForProspectiveValidation":false,"clinicalUseStatus":"prohibited"},"nextActions":["Complete Pass110 with real deidentified cohort files, completed source manifest, and governed v24 score artifacts.","Do not open prospective enrollment until Pass110 reports ready_for_prospective_validation=true.","Use the Pass111 event template to pre-align sites on endpoint lock, adjudicator count, subgroup fields, and safety/deviation capture.","Keep disease-detection, diagnosis, treatment-support, and patient-specific interpretation claims 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Pass 29 begins augmented retraining and confirms the current boundary limitation remains.","sizeBytes":5266,"href":"/repo/research-passes/pass-29-augmented-v5-training-boundary-check.html","tags":["augmented","training","boundary","check"]},{"passNumber":30,"fileName":"pass-30-dualzone-boundary-policy-and-v5-equivalence.html","title":"Pass 30 Research: Dual-Zone Boundary Policy and v5 Equivalence","summary":"This pass deepens boundary handling by testing a second low-score rescue window for AFF4-like genes, then stress-tests generalization with train-only LOSO tuning and compares augmented v5 model behavior against the existing v4-hybrid operating path.","generatedAt":"2026-05-16 (UTC). Pass 30 adds a provisional dual-zone boundary hypothesis and formal generalization gating.","sizeBytes":6977,"href":"/repo/research-passes/pass-30-dualzone-boundary-policy-and-v5-equivalence.html","tags":["dualzone","boundary","policy","and","equivalence"]},{"passNumber":31,"fileName":"pass-31-set8-external-and-v6-augmentation.html","title":"Pass 31 Research: set8 External Validation and v6 Augmentation","summary":"This pass introduces a new set8 cohort, validates policy behavior on it, then trains a v6 candidate model on base+all7 and evaluates against set8 with explicit overlap accounting.","generatedAt":"2026-05-16 (UTC). Pass 31 extends external validation and starts v6 model augmentation with overlap-aware interpretation.","sizeBytes":7100,"href":"/repo/research-passes/pass-31-set8-external-and-v6-augmentation.html","tags":["set8","external","and","augmentation"]},{"passNumber":32,"fileName":"pass-32-disjoint-set9b-and-v6-v4-check.html","title":"Pass 32 Research: Disjoint set9b and v6-v4 Check","summary":"This pass builds a new disjoint cohort family (set9 -> corrected set9b), enforces control-purity correction, and runs a strict v6-v4 comparison on a cohort with zero train-gene overlap.","generatedAt":"2026-05-16 (UTC). Pass 32 adds a purity-corrected, fully disjoint external cohort and overlap-free model comparison evidence.","sizeBytes":6087,"href":"/repo/research-passes/pass-32-disjoint-set9b-and-v6-v4-check.html","tags":["disjoint","set9b","and","check"]},{"passNumber":33,"fileName":"pass-33-set10-disjoint-boundary-and-v6-lift.html","title":"Pass 33 Research: set10 Disjoint Boundary and v6 Lift","summary":"This pass expands the disjoint external evidence with a new set10 cohort using unseen positives and a newly probed clean control block, then measures threshold sensitivity and paired v6-v4 behavior.","generatedAt":"2026-05-16 (UTC). Pass 33 deepens disjoint external evidence with a controlled boundary-stress cohort and paired v6-v4 lift checks.","sizeBytes":6193,"href":"/repo/research-passes/pass-33-set10-disjoint-boundary-and-v6-lift.html","tags":["set10","disjoint","boundary","and","lift"]},{"passNumber":34,"fileName":"pass-34-set11-zonea-external-and-v7-training.html","title":"Pass 34 Research: set11 Zone-A External and v7 Training","summary":"This pass builds a new fully disjoint cohort (set11) designed to externally test zone-A rescue behavior, then trains a v7 candidate model on base+all10 with set11 as holdout.","generatedAt":"2026-05-16 (UTC). Pass 34 provides external zone-A rescue evidence and extends augmented model training to v7.","sizeBytes":6393,"href":"/repo/research-passes/pass-34-set11-zonea-external-and-v7-training.html","tags":["set11","zonea","external","and","training"]},{"passNumber":35,"fileName":"pass-35-source-expansion-and-docker-tooling.html","title":"Pass 35 Research: Source Expansion and Docker Tooling","summary":"This pass extends discovery beyond the original source set by running live probes against OpenAlex, Europe PMC, UniProt, RCSB PDB, and Open Targets GraphQL, then validates containerized tooling paths for 3D and molecular workflows.","generatedAt":"2026-05-16 (UTC). Pass 35 adds external source expansion probes and Dockerized tooling discovery for next-stage experimentation.","sizeBytes":6275,"href":"/repo/research-passes/pass-35-source-expansion-and-docker-tooling.html","tags":["source","expansion","and","docker","tooling"]},{"passNumber":36,"fileName":"pass-36-nextjs-frontend-and-interactive-viewer.html","title":"Pass 36 Research: Next.js Frontend and Interactive Viewer","summary":"This pass creates a dedicated Next.js frontend to browse all research passes, training artifacts, model files, and open questions from one interface with a 3D interactive hero scene.","generatedAt":"2026-05-16 (UTC). Pass 36 introduces a live interactive frontend for research consumption and exploration.","sizeBytes":5355,"href":"/repo/research-passes/pass-36-nextjs-frontend-and-interactive-viewer.html","tags":["nextjs","frontend","and","interactive","viewer"]},{"passNumber":37,"fileName":"pass-37-deep-source-and-docker-runtime-validation.html","title":"Pass 37 Research: Deep Source Expansion and Docker Runtime Validation","summary":"This pass extends research depth with cursor-based multi-page pulls from OpenAlex and Europe PMC, direct AlphaFold API verification for PTEN, ChEMBL PTEN target/activity extraction, ClinVar FTP metadata harvesting, and runtime Docker validation for bio/3D toolchains.","generatedAt":"2026-05-16 (UTC). Pass 37 deepens literature/structure/chemistry coverage and adds concrete runtime-toolchain validation details.","sizeBytes":7182,"href":"/repo/research-passes/pass-37-deep-source-and-docker-runtime-validation.html","tags":["deep","source","and","docker","runtime","validation"]},{"passNumber":38,"fileName":"pass-38-frontend-molecular-workbench-and-pulse-upgrade.html","title":"Pass 38 Research: Frontend Molecular Workbench and Pulse Upgrade","summary":"This pass upgrades the Next.js research frontend with a second high-value interactive 3D surface: an embedded PDBe Mol* workbench for PDB and AlphaFold model exploration, plus live external-source pulse cards sourced from pass35/pass37 metrics.","generatedAt":"2026-05-16 (UTC). Pass 38 improves evidence browsing with domain-specific structural visualization and richer cross-source pulse visibility.","sizeBytes":4662,"href":"/repo/research-passes/pass-38-frontend-molecular-workbench-and-pulse-upgrade.html","tags":["frontend","molecular","workbench","and","pulse","upgrade"]},{"passNumber":39,"fileName":"pass-39-structure-atlas-and-docker-bio3d-pipeline.html","title":"Pass 39 Research: Structure Atlas Mapping and Docker Bio/3D Pipeline","summary":"This pass mapped the expanded gene cohort to UniProt, AlphaFold, and RCSB to create a structure-atlas layer, and added Dockerized probes for NCBI datasets gene metadata and OpenUSD runtime validation.","generatedAt":"2026-05-16 (UTC). Pass 39 established gene-level structure coverage and seeded data for subsequent frontend and runtime-deepening passes.","sizeBytes":4535,"href":"/repo/research-passes/pass-39-structure-atlas-and-docker-bio3d-pipeline.html","tags":["structure","atlas","and","docker","bio3d","pipeline"]},{"passNumber":40,"fileName":"pass-40-deep-sources-docker-3d-and-knowledge-graph.html","title":"Pass 40 Research: Deep Source Expansion, Docker 3D/Bio Pipeline, and Frontend Knowledge Graph","summary":"This pass adds a second deep source sweep (OpenAlex developer-download context, Europe PMC evaluation metadata, MolViewStories capabilities, and RCSB cross-model probes), executes a reproducible Dockerized 3D/biology pipeline (Blender render, OpenUSD stage generation, mmCIF parsing, and NCBI gene package download), and upgrades the Next.js frontend with a new 3D knowledge graph + structure atlas explorer.","generatedAt":"2026-05-16 (UTC). Pass 40 extends evidence-source breadth, validates practical Dockerized 3D/biology tooling, and improves research navigation UX with richer interactive frontend surfaces.","sizeBytes":8267,"href":"/repo/research-passes/pass-40-deep-sources-docker-3d-and-knowledge-graph.html","tags":["deep","sources","docker","and","knowledge","graph"]},{"passNumber":41,"fileName":"pass-41-3dbeacons-esmfold-openbabel-and-graph-controls.html","title":"Pass 41 Research: 3D-Beacons + ESMFold + OpenBabel/Gemmi/MRC Docker Toolchain","summary":"This pass expands external knowledge into structure-network metadata (3D-Beacons), sequence-to-structure inference interfaces (ESMFold API), and practical molecular-volume runtime tooling (OpenBabel, Gemmi, mrcfile in Docker), then upgrades the frontend 3D knowledge graph with group filters and search controls.","generatedAt":"2026-05-16 (UTC). Pass 41 extends structure-network/API coverage and adds practical molecule/volume runtime probes plus richer frontend graph controls.","sizeBytes":6918,"href":"/repo/research-passes/pass-41-3dbeacons-esmfold-openbabel-and-graph-controls.html","tags":["3dbeacons","esmfold","openbabel","and","graph","controls"]},{"passNumber":42,"fileName":"pass-42-api-contracts-runtime-and-command-center.html","title":"Pass 42 Research: API Contracts + Docker Runtime Probes + Frontend Command Center","summary":"This pass deepens research from endpoint-discovery into explicit API contracts (3D-Beacons + RCSB VolumeServer), validates runtime behavior with a new Docker toolchain layer (OpenMM, OpenUSD Exchange, Biopython, VolumeServer payload fetch, 3D-Beacons probe), and adds a frontend command-center panel for interactive inspection.","generatedAt":"2026-05-16 (UTC). Pass 42 adds explicit API-contract coverage, reproducible runtime probes across molecular/3D tools, and a higher-control interactive frontend command center.","sizeBytes":7913,"href":"/repo/research-passes/pass-42-api-contracts-runtime-and-command-center.html","tags":["api","contracts","runtime","and","command","center"]},{"passNumber":43,"fileName":"pass-43-blender-mdtoolchain-and-3d-pass-navigator.html","title":"Pass 43 Research: Blender + Molecular Toolchain Runtime + 3D Pass Navigator","summary":"This pass extends runtime experimentation into Blender 5.x headless rendering and molecular-analysis stacks (MDAnalysis, MDTraj, Biotite, PDBFixer), adds deeper source documentation coverage for those tools, and upgrades the frontend with a new 3D pass-helix navigator plus a dedicated pass43 toolchain workbench.","generatedAt":"2026-05-16 (UTC). Pass 43 expands runtime molecular/3D tool validation and upgrades frontend navigation with a new interactive 3D pass helix.","sizeBytes":6983,"href":"/repo/research-passes/pass-43-blender-mdtoolchain-and-3d-pass-navigator.html","tags":["blender","mdtoolchain","and","pass","navigator"]},{"passNumber":44,"fileName":"pass-44-web-evidence-policy-and-v8-launch.html","title":"Pass 44 Addendum - Web-evidence policy closure and v8 launch plan","summary":"Resolved all previously pending direction questions using external evidence on model trustworthiness, validation methodology, structure tooling, source API constraints, and security controls; converted decisions into an immediate research execution plan.","generatedAt":"2026-05-20 08:35 UTC","sizeBytes":10916,"href":"/repo/research-passes/pass-44-web-evidence-policy-and-v8-launch.html","tags":["web","evidence","policy","and","launch"]},{"passNumber":45,"fileName":"pass-45-v8-corrected-training-and-significance.html","title":"Pass 45 Addendum - v8 corrected-cohort training and significance check","summary":"Continued research by training a new v8 model on corrected pooled cohorts, adding a boosted-tree challenger in candidate selection, and running paired significance checks against v7 , v6 , and v4 on set11.","generatedAt":"2026-05-22 06:05 UTC","sizeBytes":5714,"href":"/repo/research-passes/pass-45-v8-corrected-training-and-significance.html","tags":["corrected","training","and","significance"]},{"passNumber":46,"fileName":"pass-46-export-bundles-and-frontend-integration.html","title":"Pass 46 Addendum - Export bundle workflow and frontend integration","summary":"Implemented pass-level snapshot exports for executive/research handoff and integrated those bundles directly into the /passes frontend so users can download JSON manifests, CSV inventories, and scene manifests without leaving the console.","generatedAt":"2026-05-22 06:16 UTC","sizeBytes":4256,"href":"/repo/research-passes/pass-46-export-bundles-and-frontend-integration.html","tags":["export","bundles","and","frontend","integration"]},{"passNumber":47,"fileName":"pass-47-executive-hero-web-paradigms-and-governance.html","title":"Pass 47 Addendum - Executive hero redesign, web design paradigms, and frontend governance","summary":"Continued research using web evidence and applied a focused frontend upgrade for executive/research readability. The result is a clearer hero narrative (research scope, current system behavior, model goal), tightened copy, and governance hardening to prevent operational logs from being exposed in the public frontend.","generatedAt":"2026-05-22 06:45 UTC","sizeBytes":6940,"href":"/repo/research-passes/pass-47-executive-hero-web-paradigms-and-governance.html","tags":["executive","hero","web","paradigms","and","governance"]},{"passNumber":48,"fileName":"pass-48-v9-neural-training-web-research-and-significance.html","title":"Pass 48 Addendum - v9 neural training continuation, web research, and significance checks","summary":"Continued the neural-network track with a dedicated v9 MLP candidate under the same corrected-cohort training and set11 holdout protocol. Also expanded web-backed research to align model selection strategy with current tabular-deep-learning evidence and transparent reporting guidance.","generatedAt":"2026-05-22 11:22 UTC","sizeBytes":10012,"href":"/repo/research-passes/pass-48-v9-neural-training-web-research-and-significance.html","tags":["neural","training","web","research","and","significance"]},{"passNumber":49,"fileName":"pass-49-v10-blended-challenger-and-question-closure.html","title":"Pass 49 Addendum - v10 blended challenger and question closure","summary":"Executed a v10 continuation pass that intentionally advances a blended soft-voting challenger (neural + tabular components), then added explicit answer closure and code-grounded verification notes to the question log so execution can continue without waiting for live responses.","generatedAt":"2026-05-22 12:20 UTC","sizeBytes":7810,"href":"/repo/research-passes/pass-49-v10-blended-challenger-and-question-closure.html","tags":["v10","blended","challenger","and","question","closure"]},{"passNumber":50,"fileName":"pass-50-v11-calibrated-challenger-and-assumption-lock.html","title":"Pass 50 Addendum - v11 calibrated challenger and assumption lock","summary":"Continued the neural/tabular model line with a calibrated blended challenger ( v11 ) and locked the next operating assumption ( Q21 ) directly in the question log so execution can proceed without waiting for interactive clarification.","generatedAt":"2026-05-22 13:30 UTC","sizeBytes":7422,"href":"/repo/research-passes/pass-50-v11-calibrated-challenger-and-assumption-lock.html","tags":["v11","calibrated","challenger","and","assumption","lock"]},{"passNumber":51,"fileName":"pass-51-combined-findings-frontend-and-self-resolved-questions.html","title":"Pass 51 Addendum - combined findings frontend and self-resolved questions","summary":"Implemented a findings-first frontend presentation that suppresses pass-by-pass browsing in primary pages, generated a consolidated research summary artifact, and resolved pending timestamped questions directly with assumed answers so research can continue without waiting on synchronous guidance.","generatedAt":"2026-05-22 14:05 UTC","sizeBytes":5885,"href":"/repo/research-passes/pass-51-combined-findings-frontend-and-self-resolved-questions.html","tags":["combined","findings","frontend","and","self","resolved"]},{"passNumber":52,"fileName":"pass-52-set12-external-holdout-and-combined-findings-refresh.html","title":"Pass 52 Addendum - set12 external holdout and combined findings refresh","summary":"Added a new disjoint external holdout cohort (set12), executed policy and paired model-significance evaluations, generated a consolidated set12 suite summary, and refreshed executive-facing frontend surfaces to show latest combined findings with explicit external-validation context.","generatedAt":"2026-05-22 13:45 UTC","sizeBytes":8428,"href":"/repo/research-passes/pass-52-set12-external-holdout-and-combined-findings-refresh.html","tags":["set12","external","holdout","and","combined","findings"]},{"passNumber":53,"fileName":"pass-53-set13-disjoint-holdout-and-bias-guardrails.html","title":"Pass 53 Addendum - set13 disjoint holdout and bias guardrails","summary":"Expanded external validation with a new disjoint set13 cohort, refreshed combined findings to prioritize the newest external slice, and added explicit guardrails for high-separation cohort bias before any model-promotion decisions.","generatedAt":"2026-05-22 14:20 UTC","sizeBytes":7729,"href":"/repo/research-passes/pass-53-set13-disjoint-holdout-and-bias-guardrails.html","tags":["set13","disjoint","holdout","and","bias","guardrails"]},{"passNumber":54,"fileName":"pass-54-set14-boundary-holdout-and-comparator-rebalancing.html","title":"Pass 54 Addendum - set14 boundary holdout and comparator rebalancing","summary":"Built a boundary-focused disjoint holdout (set14) to stress threshold robustness, observed meaningful recall drop versus high-separation slices, and rebalanced continuation guidance to treat boundary cohorts as promotion-gating blockers with explicit label-confidence caveats.","generatedAt":"2026-05-22 14:40 UTC","sizeBytes":8444,"href":"/repo/research-passes/pass-54-set14-boundary-holdout-and-comparator-rebalancing.html","tags":["set14","boundary","holdout","and","comparator","rebalancing"]},{"passNumber":55,"fileName":"pass-55-set15-mixed-boundary-validation-and-evidence-refresh.html","title":"Pass 55 Addendum - set15 mixed-boundary validation and evidence refresh","summary":"Added a new disjoint mixed-boundary cohort (set15), ran full policy/model comparator significance, and updated executive surfaces to show latest combined findings while explicitly classifying set15 as optimistic consistency evidence rather than promotion evidence.","generatedAt":"2026-05-22 14:55 UTC","sizeBytes":9340,"href":"/repo/research-passes/pass-55-set15-mixed-boundary-validation-and-evidence-refresh.html","tags":["set15","mixed","boundary","validation","and","evidence"]},{"passNumber":56,"fileName":"pass-56-set16-hard-boundary-and-ceiling-parity-analysis.html","title":"Pass 56 Addendum - set16 hard-boundary and ceiling-parity analysis","summary":"Constructed a stricter disjoint set16 cohort from extended probe sources, excluded known neurologic-metabolism confounders from controls, and re-ran full policy/comparator significance. Outcome remained ceiling-level parity, so promotion remains blocked pending a lower-confidence boundary-focused set17.","generatedAt":"2026-05-22 15:20 UTC","sizeBytes":8470,"href":"/repo/research-passes/pass-56-set16-hard-boundary-and-ceiling-parity-analysis.html","tags":["set16","hard","boundary","and","ceiling","parity"]},{"passNumber":57,"fileName":"pass-57-set17-boundary-stress-and-non-ceiling-separation.html","title":"Pass 57 Addendum - set17 boundary stress and non-ceiling separation","summary":"Built set17 as an intentionally harder disjoint stress cohort using low-confidence positives and high-risk controls. This pass breaks the ceiling behavior seen in set15/set16 and exposes threshold failures, while still showing parity between v11 and tabular anchors (v8/v7/v6/v4).","generatedAt":"2026-05-22 15:38 UTC","sizeBytes":9639,"href":"/repo/research-passes/pass-57-set17-boundary-stress-and-non-ceiling-separation.html","tags":["set17","boundary","stress","and","non","ceiling"]},{"passNumber":58,"fileName":"pass-58-set18-adjudicated-expansion-and-gating.html","title":"Pass 58 Addendum - set18 adjudicated expansion and gating","summary":"Expanded to a larger disjoint set18 cohort with adjudicated low-confidence positives and hard controls. Outcome: v11 degraded on this slice and no longer tied with anchors; `v4_rf` is best at threshold 0.38, so promotion remains blocked and comparator governance is tightened.","generatedAt":"2026-05-22 16:04 UTC","sizeBytes":9754,"href":"/repo/research-passes/pass-58-set18-adjudicated-expansion-and-gating.html","tags":["set18","adjudicated","expansion","and","gating"]},{"passNumber":59,"fileName":"pass-59-set19-expanded-adjudicated-and-executive-frontend-refresh.html","title":"Pass 59 Addendum - set19 adjudicated expansion and executive frontend refresh","summary":"Expanded to a 40-row disjoint adjudicated set19 cohort and refreshed executive-facing frontend copy/design to emphasize latest combined findings, model-governance clarity, and biomarker-program narrative. Outcome: v11 remains non-leading on external gating, with v7_rf best at threshold 0.38.","generatedAt":"2026-05-22 16:15 UTC","sizeBytes":11656,"href":"/repo/research-passes/pass-59-set19-expanded-adjudicated-and-executive-frontend-refresh.html","tags":["set19","expanded","adjudicated","and","executive","frontend"]},{"passNumber":60,"fileName":"pass-60-set20-replication-governance-and-executive-simplification.html","title":"Pass 60 Addendum - set20 replication, governance, and executive simplification","summary":"Completed a 48-row disjoint adjudicated replication cohort (set20), refreshed combined findings logic, and simplified executive-facing frontend surfaces to latest/combined outputs only. Outcome: v4_rf remains the best external performer at threshold 0.38; v11 remains research-only.","generatedAt":"2026-05-22 16:45 UTC","sizeBytes":11972,"href":"/repo/research-passes/pass-60-set20-replication-governance-and-executive-simplification.html","tags":["set20","replication","governance","and","executive","simplification"]},{"passNumber":61,"fileName":"pass-61-set21-lowconfidence-replication-and-executive-hierarchy-refresh.html","title":"Pass 61 Addendum - set21 low-confidence replication and executive hierarchy refresh","summary":"Completed a new disjoint external cohort (set21) designed to stress boundary behavior with low-confidence positives and hard controls, then updated executive-facing frontend hierarchy and copy using current UX/accessibility guidance. Outcome: v4_rf remains best on set21 at threshold 0.38 and v11 remains research-only.","generatedAt":"2026-05-22 17:20 UTC","sizeBytes":10671,"href":"/repo/research-passes/pass-61-set21-lowconfidence-replication-and-executive-hierarchy-refresh.html","tags":["set21","lowconfidence","replication","and","executive","hierarchy"]},{"passNumber":62,"fileName":"pass-62-set22-replication-pressure-and-anchor-governance-continuation.html","title":"Pass 62 Addendum - set22 replication pressure and anchor-governance continuation","summary":"Built and evaluated set22 as the next disjoint external continuation cohort under low-confidence/hard-control pressure. Outcome remains governance-consistent: v4_rf is external-best at threshold 0.38 and v11 remains a research-only challenger.","generatedAt":"2026-05-22 17:40 UTC","sizeBytes":9883,"href":"/repo/research-passes/pass-62-set22-replication-pressure-and-anchor-governance-continuation.html","tags":["set22","replication","pressure","and","anchor","governance"]},{"passNumber":63,"fileName":"pass-63-set23-anti-ceiling-replication-and-boundary-governance.html","title":"Pass 63 Addendum - set23 anti-ceiling replication and boundary-governance continuation","summary":"Built and evaluated set23 as a stricter anti-ceiling continuation cohort with lower-confidence positives and harder controls. Outcome remains governance-consistent: v4_rf is external-best at threshold 0.38 and v11 remains a research-only challenger.","generatedAt":"2026-05-22 18:05 UTC","sizeBytes":10278,"href":"/repo/research-passes/pass-63-set23-anti-ceiling-replication-and-boundary-governance.html","tags":["set23","anti","ceiling","replication","and","boundary"]},{"passNumber":64,"fileName":"pass-64-set24-boundary-continuation-and-frontend-refresh.html","title":"Pass 64 Addendum - set24 boundary continuation and frontend refresh","summary":"Built set24 as a fresh disjoint continuation after set23 exposed wider v11 boundary failures. Outcome remains governance-conservative: v6_rf is external-best at threshold 0.38, and v11_cal remains a research-only challenger.","generatedAt":"2026-05-29 16:05 UTC","sizeBytes":7720,"href":"/repo/research-passes/pass-64-set24-boundary-continuation-and-frontend-refresh.html","tags":["set24","boundary","continuation","and","frontend","refresh"]},{"passNumber":65,"fileName":"pass-65-set25-boundary-replication-and-editorial-frontend-refresh.html","title":"Pass 65 Addendum - set25 boundary replication and editorial frontend refresh","summary":"Built set25 as a 48-row disjoint replication after set24. The result remains promotion-blocking: v4_rf is external-best at threshold 0.38, while v11_cal drops to 35.9% F1. The overview frontend was also rebuilt around the shared dark editorial design reference.","generatedAt":"2026-05-29 16:40 UTC","sizeBytes":6235,"href":"/repo/research-passes/pass-65-set25-boundary-replication-and-editorial-frontend-refresh.html","tags":["set25","boundary","replication","and","editorial","frontend"]},{"passNumber":66,"fileName":"pass-66-set26-boundary-replication.html","title":"Pass 66 Addendum - set26 boundary replication","summary":"Built set26 as a fresh 48-row disjoint replication after set25. The result keeps governance anchor-first: v4_rf remains the best threshold-0.38 performer, while v11_cal improves versus v10/v9 but does not exceed the strongest anchors.","generatedAt":"2026-05-29 20:50 UTC","sizeBytes":5788,"href":"/repo/research-passes/pass-66-set26-boundary-replication.html","tags":["set26","boundary","replication"]},{"passNumber":67,"fileName":"pass-67-v12-hard-cohort-gap-analysis.html","title":"Pass 67 Addendum - v12 hard-cohort gap analysis","summary":"Added a reproducible gap-analysis artifact for the recent hard-window cohorts set24 , set25 , and set26 . The result confirms that v11_cal is not ready for promotion and turns the failure pattern into explicit v12 experiment requirements.","generatedAt":"2026-06-01 UTC","sizeBytes":4894,"href":"/repo/research-passes/pass-67-v12-hard-cohort-gap-analysis.html","tags":["v12","hard","cohort","gap","analysis"]},{"passNumber":68,"fileName":"pass-68-v12-family-weighted-neural-challenger.html","title":"Pass 68 Addendum - v12 family-weighted neural challenger","summary":"Trained a first v12 neural challenger using the pass-67 hard-cohort failure families as sample-weight guidance while keeping set24-set26 evaluation-only. The model is useful as a negative result: positive family weighting alone is not enough because hard-control specificity collapses.","generatedAt":"2026-06-01 UTC","sizeBytes":4886,"href":"/repo/research-passes/pass-68-v12-family-weighted-neural-challenger.html","tags":["v12","family","weighted","neural","challenger"]},{"passNumber":69,"fileName":"pass-69-v13-hard-control-aware-challenger.html","title":"Pass 69 Addendum - v13 hard-control-aware challenger","summary":"Trained a hard-control-weighted random-forest challenger to answer the pass-68 specificity failure. V13 uses only existing base/all10 rows, filters set24-set26 overlap out of training, and weights proxy hard controls with high general disease burden but low NDD-specific signal.","generatedAt":"2026-06-01 UTC","sizeBytes":4818,"href":"/repo/research-passes/pass-69-v13-hard-control-aware-challenger.html","tags":["v13","hard","control","aware","challenger"]},{"passNumber":70,"fileName":"pass-70-set27-v13-fresh-replication-gate.html","title":"Pass 70 Addendum - set27 v13 fresh replication gate","summary":"Built a new 48-row disjoint external cohort to test whether the v13 hard-control-aware challenger generalizes beyond the set24-set26 hard window. set27 is balanced at 24 target-family positives and 24 hard controls, disjoint from all10 and prospective sets 12 through 26.","generatedAt":"2026-06-01 UTC","sizeBytes":4838,"href":"/repo/research-passes/pass-70-set27-v13-fresh-replication-gate.html","tags":["set27","v13","fresh","replication","gate"]},{"passNumber":71,"fileName":"pass-71-v14-specificity-gated-ensemble.html","title":"Pass 71 Addendum - v14 specificity-gated ensemble","summary":"Built a conservative v14 challenger that gates v13 through high-confidence rescue paths and the high-specificity v4 anchor. Thresholds were selected on set24-set26 only; set27 remains the fresh readout for this pass.","generatedAt":"2026-06-01 UTC","sizeBytes":4878,"href":"/repo/research-passes/pass-71-v14-specificity-gated-ensemble.html","tags":["v14","specificity","gated","ensemble"]},{"passNumber":72,"fileName":"pass-72-v15-neural-rescue-gate.html","title":"Pass 72 Addendum - v15 neural recall-rescue gate","summary":"Trained a weighted MLP neural component and wrapped it around the v14 specificity gate to test whether a neural rescue path can recover set27 low-confidence positives without reopening hard-control false positives.","generatedAt":"2026-06-01 UTC","sizeBytes":4845,"href":"/repo/research-passes/pass-72-v15-neural-rescue-gate.html","tags":["v15","neural","rescue","gate"]},{"passNumber":73,"fileName":"pass-73-set28-reserve-replication-gate.html","title":"Pass 73 Addendum - set28 reserve replication gate","summary":"Built a 48-row set28 reserve holdout from unused set20-set27 probe candidates after blocking all10 and set12-set27 labels. The purpose is to test whether the v15/v14 behavior from set27 is stable on another disjoint cohort.","generatedAt":"2026-06-01 UTC","sizeBytes":4742,"href":"/repo/research-passes/pass-73-set28-reserve-replication-gate.html","tags":["set28","reserve","replication","gate"]},{"passNumber":74,"fileName":"pass-74-v16-evidence-gated-rescue.html","title":"Pass 74 Addendum - v16 evidence-gated rescue","summary":"Trained an evidence-gated v16 challenger that keeps v4 as the specificity anchor and only lets v15 rescue low-v4 genes when NDD-specific literature and association evidence pass a conservative gate.","generatedAt":"2026-06-01 UTC","sizeBytes":4678,"href":"/repo/research-passes/pass-74-v16-evidence-gated-rescue.html","tags":["v16","evidence","gated","rescue"]},{"passNumber":75,"fileName":"pass-75-set29-fresh-replication.html","title":"Pass 75 Addendum - set29 fresh replication","summary":"Built a fresh live-scored set29 cohort after blocking all10 and set12-set28 labels, then used it as a harder replication gate for the v16 evidence-gated rescue and older neural/tabular comparators.","generatedAt":"2026-06-01 UTC","sizeBytes":4975,"href":"/repo/research-passes/pass-75-set29-fresh-replication.html","tags":["set29","fresh","replication"]},{"passNumber":76,"fileName":"pass-76-v17-stacked-representation.html","title":"Pass 76 Addendum - v17 stacked representation","summary":"Trained a stacked MLP challenger that combines prior model scores with NDD-specificity ratios. The model uses labels available through set28 and holds set29 out as fresh validation.","generatedAt":"2026-06-01 UTC","sizeBytes":4749,"href":"/repo/research-passes/pass-76-v17-stacked-representation.html","tags":["v17","stacked","representation"]},{"passNumber":77,"fileName":"pass-77-set30-hard-control-replication.html","title":"Pass 77 Addendum - set30 hard-control replication","summary":"Built a fresh, disjoint set30 gate around v17 residual neighborhoods: NDD-associated developmental and RASopathy positives against difficult ataxia, leukodystrophy, neuropathy, and movement-disorder controls.","generatedAt":"2026-06-01 UTC","sizeBytes":5171,"href":"/repo/research-passes/pass-77-set30-hard-control-replication.html","tags":["set30","hard","control","replication"]},{"passNumber":78,"fileName":"pass-78-v18-hard-negative-training.html","title":"Pass 78 Addendum - v18 hard-negative training","summary":"Trained a stacked neural challenger with v17 as an input component and set30 controls upweighted as explicit hard negatives. Set29 remains excluded from training as the preservation holdout.","generatedAt":"2026-06-01 UTC","sizeBytes":5201,"href":"/repo/research-passes/pass-78-v18-hard-negative-training.html","tags":["v18","hard","negative","training"]},{"passNumber":79,"fileName":"pass-79-set31-v18-replication.html","title":"Pass 79 Addendum - set31 v18 replication","summary":"Built a fresh, disjoint set31 gate after v18 consumed set30 as training data. The cohort tests whether v18's hard-negative specificity repair generalizes to new developmental positives and hard neurological controls.","generatedAt":"2026-06-01 UTC","sizeBytes":4954,"href":"/repo/research-passes/pass-79-set31-v18-replication.html","tags":["set31","v18","replication"]},{"passNumber":80,"fileName":"pass-80-v19-set31-recall-rescue.html","title":"Pass 80 Addendum - v19 set31 recall rescue","summary":"Trained a v19 neural challenger that consumes set31 as recall-rescue training data, stacks v18 as a component, and adds v18 suppression-gap features to recover the set31 false-negative cluster.","generatedAt":"2026-06-01 UTC","sizeBytes":5275,"href":"/repo/research-passes/pass-80-v19-set31-recall-rescue.html","tags":["v19","set31","recall","rescue"]},{"passNumber":81,"fileName":"pass-81-set32-v19-replication.html","title":"Pass 81 Addendum - set32 v19 replication","summary":"Built a fresh, disjoint set32 gate after v19 consumed set31 as recall-rescue training data. The cohort tests whether v19's recall repair generalizes while preserving v18 hard-control specificity.","generatedAt":"2026-06-01 UTC","sizeBytes":4831,"href":"/repo/research-passes/pass-81-set32-v19-replication.html","tags":["set32","v19","replication"]},{"passNumber":82,"fileName":"pass-82-v20-set32-specificity-repair.html","title":"Pass 82 Addendum - v20 set32 specificity repair","summary":"Trained a v20 stacked neural challenger that consumes set32 as specificity-repair training data, stacks v19 and v18, and adds v19 overcall features to reduce hard-control false positives.","generatedAt":"2026-06-01 UTC","sizeBytes":5108,"href":"/repo/research-passes/pass-82-v20-set32-specificity-repair.html","tags":["v20","set32","specificity","repair"]},{"passNumber":83,"fileName":"pass-83-set33-v20-replication.html","title":"Pass 83 Addendum - set33 v20 replication gate","summary":"Built a fresh set33 live-scored validation gate after v20 consumed set32 as specificity-repair training data. The cohort is disjoint from all10 and set12-set32 and keeps the 24 positive / 24 control evaluation balance.","generatedAt":"2026-06-01 UTC","sizeBytes":5631,"href":"/repo/research-passes/pass-83-set33-v20-replication.html","tags":["set33","v20","replication"]},{"passNumber":84,"fileName":"pass-84-v21-set33-failure-repair.html","title":"Pass 84 Addendum - v21 set33 failure repair","summary":"Trained a v21 stacked neural repair model that consumes set33 as training evidence, stacks v20 and v17 behavior, and adds targeted set33 recall-gap plus hard-control features.","generatedAt":"2026-06-01 UTC","sizeBytes":5430,"href":"/repo/research-passes/pass-84-v21-set33-failure-repair.html","tags":["v21","set33","failure","repair"]},{"passNumber":85,"fileName":"pass-85-set34-v21-replication.html","title":"Pass 85 Addendum - set34 v21 replication gate","summary":"Built the first fresh external gate after v21 consumed set33 labels. The set34 cohort is balanced, disjoint from all10 and set12-set33, and targeted at low-confidence positives plus hard disease controls.","generatedAt":"2026-06-01 UTC","sizeBytes":5165,"href":"/repo/research-passes/pass-85-set34-v21-replication.html","tags":["set34","v21","replication"]},{"passNumber":86,"fileName":"pass-86-v22-set34-residual-repair.html","title":"Pass 86 Addendum - v22 set34 residual repair","summary":"Trained a v22 neural residual-repair layer over v21 component scores using set34 false-negative and false-positive failures. Set29 remains held out as preservation validation; set35 is now required as the next fresh replication gate.","generatedAt":"2026-06-01 UTC","sizeBytes":5398,"href":"/repo/research-passes/pass-86-v22-set34-residual-repair.html","tags":["v22","set34","residual","repair"]},{"passNumber":87,"fileName":"pass-87-set35-v22-replication.html","title":"Pass 87 Addendum - set35 v22 replication gate","summary":"Built the first fresh external gate after v22 consumed set34 residual-repair labels. Set35 is balanced, disjoint from all10 and set12-set34, and tests whether v22 generalizes beyond its repair-training cohort.","generatedAt":"2026-06-01 UTC","sizeBytes":6075,"href":"/repo/research-passes/pass-87-set35-v22-replication.html","tags":["set35","v22","replication"]},{"passNumber":88,"fileName":"pass-88-v23-set35-comparator-repair.html","title":"Pass 88 Addendum - v23 set35 comparator repair","summary":"Trained a v23 neural comparator-repair layer over v22 using set35 residual errors and v16/v4 consensus signals. Set35 is now training-fit evidence for v23; set36 is required as the next fresh replication gate.","generatedAt":"2026-06-01 UTC","sizeBytes":5717,"href":"/repo/research-passes/pass-88-v23-set35-comparator-repair.html","tags":["v23","set35","comparator","repair"]},{"passNumber":89,"fileName":"pass-89-set36-v23-replication.html","title":"Pass 89 Addendum - set36 v23 replication","summary":"Built a fresh 48-row set36 replication cohort after v23 consumed set35 as comparator-repair training evidence. Set36 tests whether the v23 repair generalizes to unseen low-confidence positives and hard controls.","generatedAt":"2026-06-01 UTC","sizeBytes":5302,"href":"/repo/research-passes/pass-89-set36-v23-replication.html","tags":["set36","v23","replication"]},{"passNumber":90,"fileName":"pass-90-source-integration-set37.html","title":"Pass 90 Addendum - source integration and set37","summary":"Built Pass 90 around adjudicated boundary data rather than random volume. The pass pulls compact evidence from GenCC, ClinGen, SFARI Gene, HPO phenotype-to-gene annotations, PanelApp, and metadata routes for gnomAD v4.1.1 constraint, BrainSpan, and PsychENCODE, then composes a larger 96-row set37 gate.","generatedAt":"2026-06-03 UTC","sizeBytes":6056,"href":"/repo/research-passes/pass-90-source-integration-set37.html","tags":["source","integration","set37"]},{"passNumber":91,"fileName":"pass-91-v24-source-feature-integration.html","title":"Pass 91 Addendum - v24 source feature integration","summary":"Pass 91 turns the Pass 90 adjudication sources into trainable model features and trains v24_source_feature_integration as a research-only challenger over the v23 stack. This is a signal-quality pass, not a model-promotion pass.","generatedAt":null,"sizeBytes":6087,"href":"/repo/research-passes/pass-91-v24-source-feature-integration.html","tags":["v24","source","feature","integration"]},{"passNumber":92,"fileName":"pass-92-set38-v24-replication.html","title":"Pass 92 Addendum - set38 v24 source-feature replication","summary":"Pass 92 builds set38 as a 144-row source-adjudicated gate after v24 consumed set37 labels. The cohort tests whether v24's source-feature integration generalizes against v23, v22, v16, and v4 on a larger fresh label set.","generatedAt":null,"sizeBytes":6539,"href":"/repo/research-passes/pass-92-set38-v24-replication.html","tags":["set38","v24","replication"]},{"passNumber":93,"fileName":"pass-93-set39-source-refresh.html","title":"Pass 93 Addendum - set39 refreshed source freeze","summary":"Pass 93 converts the set38 caveat into a concrete data plan: build set39 candidates from a refreshed official PanelApp cardiac-control pull plus withheld Pass90 positive and hard-negative evidence.","generatedAt":null,"sizeBytes":5051,"href":"/repo/research-passes/pass-93-set39-source-refresh.html","tags":["set39","source","refresh"]},{"passNumber":94,"fileName":"pass-94-set39-v24-replication.html","title":"Pass 94 Addendum - set39 v24 refreshed-source replication","summary":"Pass 94 scores the Pass 93 refreshed/withheld set39 source freeze and evaluates v24_sf against v23_cr, v22_rr, v16_eg, and v4_rf. The gate is balanced, disjoint through set38, and intentionally enriched for cardiac hard-negative controls.","generatedAt":null,"sizeBytes":6698,"href":"/repo/research-passes/pass-94-set39-v24-replication.html","tags":["set39","v24","replication"]},{"passNumber":95,"fileName":"pass-95-v24-calibration-stability.html","title":"Pass 95 Addendum - v24 calibration and set40 freeze","summary":"Pass 95 converts the set39 replication result into operating-threshold evidence and a fresh set40 candidate pool. It tests whether v24's threshold can improve specificity without losing recall across the two strongest fresh gates: set38 and set39.","generatedAt":null,"sizeBytes":6132,"href":"/repo/research-passes/pass-95-v24-calibration-stability.html","tags":["v24","calibration","stability"]},{"passNumber":96,"fileName":"pass-96-set40-v24-candidate-threshold.html","title":"Pass 96 Addendum - set40 v24 candidate-threshold gate","summary":"Pass 96 live-scores the Pass95 set40 candidate pool and evaluates v24 at the 0.38 anchor, 0.50 reference point, and 0.71 candidate threshold.","generatedAt":null,"sizeBytes":6075,"href":"/repo/research-passes/pass-96-set40-v24-candidate-threshold.html","tags":["set40","v24","candidate","threshold"]},{"passNumber":97,"fileName":"pass-97-v24-governance-package.html","title":"Pass 97 Addendum - v24 governance package","summary":"Pass 97 packages the v24 promotion candidate into a reproducible governance handoff with hashed artifacts, operating-threshold policy, residual monitoring, and explicit release blockers.","generatedAt":null,"sizeBytes":5358,"href":"/repo/research-passes/pass-97-v24-governance-package.html","tags":["v24","governance","package"]},{"passNumber":98,"fileName":"pass-98-set41-surveillance.html","title":"Pass 98 Addendum - set41 v24 surveillance","summary":"Pass 98 builds and scores a fresh set41 surveillance cohort for the governed v24 candidate. The goal is not more random data; it is adjudicated boundary data plus hard negative controls.","generatedAt":null,"sizeBytes":6740,"href":"/repo/research-passes/pass-98-set41-surveillance.html","tags":["set41","surveillance"]},{"passNumber":99,"fileName":"pass-99-final-governance-signoff.html","title":"Pass 99 Addendum - v24 final governance sign-off","summary":"Pass 99 converts the set41 surveillance pass into a final governed research-triage sign-off with manifest replay, endpoint readiness, and a smoke-tested v24 scorer.","generatedAt":null,"sizeBytes":5844,"href":"/repo/research-passes/pass-99-final-governance-signoff.html","tags":["final","governance","signoff"]},{"passNumber":100,"fileName":"pass-100-clinical-translation-readiness.html","title":"Pass 100 Addendum - clinical translation readiness","summary":"Pass 100 is the first post-sign-off clinical-translation gate. It preserves the Pass99 research-triage approval while explicitly blocking disease-detection and diagnosis claims until patient-level validation and clinical controls exist.","generatedAt":null,"sizeBytes":5627,"href":"/repo/research-passes/pass-100-clinical-translation-readiness.html","tags":["clinical","translation","readiness"]},{"passNumber":101,"fileName":"pass-101-patient-level-validation-readiness.html","title":"Pass 101 Addendum - patient-level validation readiness","summary":"Pass 101 advances the project from a clinical blocker statement to an executable validation package. It locks the retrospective patient-level endpoint, cohort schema, cohort CSV template, and validation runner while preserving the rule that clinical disease-detection use is prohibited.","generatedAt":null,"sizeBytes":6554,"href":"/repo/research-passes/pass-101-patient-level-validation-readiness.html","tags":["patient","level","validation","readiness"]},{"passNumber":102,"fileName":"pass-102-retrospective-validation-runner.html","title":"Pass 102 Addendum - retrospective validation runner","summary":"Pass102 turns the Pass101 patient-level protocol into an executable analysis path. It joins deidentified cohort rows to frozen v24 scores, computes locked threshold metrics, extracts residuals, suppresses underpowered subgroup strata, and keeps clinical use prohibited.","generatedAt":null,"sizeBytes":5477,"href":"/repo/research-passes/pass-102-retrospective-validation-runner.html","tags":["retrospective","validation","runner"]},{"passNumber":103,"fileName":"pass-103-external-validation-intake.html","title":"Pass 103 Addendum - external validation intake","summary":"Pass103 freezes the external patient-level validation handoff. It validates a Pass101-compatible cohort, writes a candidate score input CSV, hashes the cohort, protocol, schema, v24 model, and score artifacts, and runs the locked Pass102 retrospective validator when score rows are present.","generatedAt":null,"sizeBytes":5513,"href":"/repo/research-passes/pass-103-external-validation-intake.html","tags":["external","validation","intake"]},{"passNumber":104,"fileName":"pass-104-retrospective-acceptance-gates.html","title":"Pass 104 Addendum - retrospective acceptance gates","summary":"Pass104 adds a locked advancement decision layer above Pass102 and Pass103. It evaluates a retrospective patient-level validation run against fixed cohort, metric, calibration, subgroup, residual-review, and prospective-validation gates before any clinical-validation advance.","generatedAt":null,"sizeBytes":5622,"href":"/repo/research-passes/pass-104-retrospective-acceptance-gates.html","tags":["retrospective","acceptance","gates"]},{"passNumber":105,"fileName":"pass-105-cohort-admissibility-audit.html","title":"Pass 105 Addendum - cohort admissibility audit","summary":"Pass105 adds a pre-evidence audit for patient cohorts. It checks whether an incoming cohort is admissible before Pass102 score joins and Pass104 retrospective acceptance gates can treat it as validation evidence.","generatedAt":null,"sizeBytes":5178,"href":"/repo/research-passes/pass-105-cohort-admissibility-audit.html","tags":["cohort","admissibility","audit"]},{"passNumber":106,"fileName":"pass-106-source-manifest-readiness.html","title":"Pass 106 Addendum - source manifest readiness","summary":"Pass106 provides the source-manifest schema, template, validator, and dry-run template check needed before an external cohort can satisfy Pass105's source-manifest gate.","generatedAt":null,"sizeBytes":5413,"href":"/repo/research-passes/pass-106-source-manifest-readiness.html","tags":["source","manifest","readiness"]},{"passNumber":107,"fileName":"pass-107-source-manifest-completion.html","title":"Pass 107 Addendum - source manifest completion","summary":"Pass107 converts the Pass106 template blocker into an owner-facing completion packet that 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rows.","generatedAt":null,"sizeBytes":4563,"href":"/repo/research-passes/pass-109-frozen-v24-scoring-handoff.html","tags":["frozen","v24","scoring","handoff"]},{"passNumber":110,"fileName":"pass-110-retrospective-validation-launch.html","title":"Pass110 Retrospective Validation Launch","summary":"Pass110 packages the end-to-end retrospective validation path as one launch sequence: Pass108 preflight, Pass103 candidate intake, Pass109 locked scoring handoff, Pass102 validation, and Pass104 acceptance gates.","generatedAt":null,"sizeBytes":3808,"href":"/repo/research-passes/pass-110-retrospective-validation-launch.html","tags":["retrospective","validation","launch"]},{"passNumber":111,"fileName":"pass-111-prospective-validation-readiness.html","title":"Pass111 Prospective Validation Readiness","summary":"Pass111 defines the governed prospective multisite validation package that follows a successful Pass110 retrospective launch: protocol, event schema, event-log template, readiness runner, metrics summary, API, and frontend reporting.","generatedAt":null,"sizeBytes":4591,"href":"/repo/research-passes/pass-111-prospective-validation-readiness.html","tags":["prospective","validation","readiness"]},{"passNumber":112,"fileName":"pass-112-retrospective-cohort-evidence-dossier.html","title":"Pass112 Retrospective Cohort Evidence Dossier","summary":"Pass112 creates the real retrospective cohort evidence packet needed before Pass108 and Pass110 can move from template-chain dry run to governed research validation execution.","generatedAt":null,"sizeBytes":4579,"href":"/repo/research-passes/pass-112-retrospective-cohort-evidence-dossier.html","tags":["retrospective","cohort","evidence","dossier"]},{"passNumber":113,"fileName":"pass-113-retrospective-execution-bundle.html","title":"Pass113 Retrospective Execution Bundle","summary":"Pass113 creates the governed launch bundle that requires Pass112 owner evidence before running Pass110 on real deidentified cohort 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future prospective cohorts prioritize `zone-A` rescue validation (more borderline positives) or raw disjoint scale (more easy-separation positives)?","why":"It determines whether we optimize for policy validation depth or sample-size expansion speed.","defaultAssumption":"Prioritize `zone-A` rescue validation first, then scale.","answer":"Prioritize `zone-A` rescue validation for the next two passes, then scale disjoint cohorts after boundary stability improves."},{"id":"Q2","question":"For corrected cohort variants, should I keep both original and corrected cohorts (e.g., `set9` + `set9b`) in training candidates, or only the corrected one?","why":"Including originals may add noise from known label-risk controls.","defaultAssumption":"Use corrected cohorts only in future augmented training bundles.","answer":"Use corrected cohorts only for training; keep original cohorts only as stress-test holdouts to reduce label-noise leakage."},{"id":"Q3","question":"Do you want me to keep the model family constrained to interpretable tabular models (logistic regression + random forest), or add gradient-boosted trees (XGBoost/LightGBM) next?","why":"Expanded model family may improve boundary recall but increases complexity and dependency footprint.","defaultAssumption":"Keep current interpretable model family for continuity.","answer":"Keep logistic regression + random forest as deployment anchors and add XGBoost/LightGBM as challenger models in the next experiment cycle."},{"id":"Q4","question":"Should I keep the deploy threshold anchor at `0.38`, or start adaptive threshold selection per cohort family?","why":"A fixed threshold is stable; adaptive thresholds may increase performance variance and reduce comparability.","defaultAssumption":"Keep `0.38` as the primary anchor and report 0.45/0.50 sensitivity.","answer":"Keep `0.38` as the primary deploy anchor and evaluate adaptive thresholds only as a secondary cohort-specific policy after external calibration checks."},{"id":"Q5","question":"Do you want me to start adding explainability artifacts (feature-attribution per gene) in each new pass?","why":"It improves interpretability but adds run time and artifact volume.","defaultAssumption":"Defer until we finalize `v7` holdout behavior.","answer":"Start lightweight explainability now in each pass (top features plus per-gene rationale summaries) and expand depth incrementally."},{"id":"Q6","question":"Should the new Next.js frontend prioritize curation workflows (editing annotations, tags, decisions) or remain a read-only evidence console for now?","why":"Editing workflows require persistence/schema changes; read-only mode can ship immediately with lower risk.","defaultAssumption":"Keep the first release read-only and optimize for fast exploration.","answer":"Keep the executive-facing frontend read-only for now and add curation/editing only in a separate authenticated back-office surface."},{"id":"Q7","question":"For 3D in the frontend, do you want me to integrate a true molecular viewer (Mol* embeddings per structure ID) in the next pass?","why":"Mol* integration increases scientific utility but adds bundle size and specialized UI complexity.","defaultAssumption":"Keep current generic 3D topology scene first, add Mol* in a dedicated follow-up pass.","answer":"Yes, integrate Mol* in the next pass with lazy loading and scoped entry points for selected genes and structures."},{"id":"Q8","question":"Should pass35 expanded source pulls (OpenAlex/EuropePMC) be promoted into the core nightly ETL pipeline?","why":"Promotion increases evidence freshness but also increases API dependencies and rate-limit surface area.","defaultAssumption":"Keep pass35 probes as optional add-on jobs until we validate stability over multiple days.","answer":"Promote these pulls as a guarded nightly canary first (not full core ETL), with strict API budgeting, rate controls, and fallback logic."},{"id":"Q9","question":"Do you want me to run a new `v8` model experiment using corrected cohorts only (set1-4 + set5b + set6 + set7b + set8 + set9b + set10 + set11)?","why":"Could improve generalization consistency, but may change score calibration and threshold behavior.","defaultAssumption":"Hold off on v8 until you confirm this cohort policy.","answer":"Yes, run `v8` on corrected cohorts only and compare against `v7` using identical external validation and threshold reports."},{"id":"Q10","question":"For OpenUSD experimentation, should I prioritize a lighter Python `usd-core` path first, or continue with heavyweight Docker images that may consume >20GB per run?","why":"Container-heavy workflows can saturate disk and destabilize other experiments on shared hosts.","defaultAssumption":"Prioritize lightweight `usd-core` and selective container probes with strict cleanup.","answer":"Prioritize lightweight Python `usd-core` first; run heavyweight container paths only for explicitly scoped validation tasks."},{"id":"Q11","question":"Do you want molecular visualization to remain exploratory (manual ID input), or should I bind it to model outputs so predicted genes automatically map to structures when available?","why":"Automatic structure mapping adds high utility but needs an accession-resolution layer and error handling for missing structures.","defaultAssumption":"Keep manual + preset exploration first, then add automated mapping in a dedicated pass.","answer":"Bind molecular visualization to model outputs next, with manual override and explicit confidence/fallback states when mappings are missing."},{"id":"Q12","question":"Should I expand structure coverage to include computed-model channels (AlphaFold complex sets/3D-Beacons) as first-class evidence in the atlas?","why":"This broadens coverage for sparse experimental genes (e.g., TLK1) but requires confidence/quality gating to avoid over-trusting predicted structures.","defaultAssumption":"Keep experimental and AlphaFold monomer paths primary, and stage complex/predicted-model integration behind explicit quality gates.","answer":"Yes, include computed-model channels as first-class evidence with confidence gating (pLDDT/PAE and provenance labels) beside experimental structures."},{"id":"Q13","question":"Should the frontend stay as an analysis console, or should I add export workflows (CSV/JSON snapshot bundles + downloadable scene manifests) in the next pass?","why":"Export workflows help collaboration and offline review, but they add UI complexity and versioning requirements for artifact reproducibility.","defaultAssumption":"Keep console-first UX and add exports only after core interactive views stabilize.","answer":"Add export workflows in the next pass (JSON/CSV snapshot bundles and scene manifests) while keeping editing disabled."},{"id":"Q14","question":"Should I integrate a true volumetric map overlay workflow next (PDBe/RCSB volume server wiring), or prioritize broader multi-protein structure retrieval first?","why":"Volumetric overlays deepen structural validation quality, while broader retrieval increases coverage and triage speed across more genes.","defaultAssumption":"Prioritize broader retrieval first, then add volumetric overlays as a focused depth pass.","answer":"Prioritize broader multi-protein structure retrieval first, then schedule volumetric overlays immediately after coverage baseline improves."},{"id":"Q15","question":"For pass43, should I prioritize targeted model training experiments using new structure-derived features (e.g., provider diversity, map payload availability), or keep training frozen and only expand source/runtime validation?","why":"Structure-derived features may improve biological signal but change model calibration and comparability against previous holdouts.","defaultAssumption":"Keep model training frozen for one more pass and expand source/runtime validation plus frontend explainability first.","answer":"Unfreeze training now for a targeted `v8` experiment with structure-derived features, while keeping strict `v7` comparator and calibration audit."},{"id":"Q16","question":"For the next pass, should I wire pass-level export bundles (selected pass reports + runtime artifacts + summary JSON) directly in the frontend, or prioritize deeper live API orchestration panels (run/poll probes from UI)?","why":"Export bundles improve collaboration/offline handoff, while live orchestration increases interactive power but requires stricter backend safety controls.","defaultAssumption":"Prioritize export bundles first, then add limited live orchestration in a guarded follow-up.","answer":"Implement pass-level export bundles first and defer live API orchestration to a guarded follow-up with RBAC and audit logging."},{"id":"Q17","question":"For executive-facing frontend copy, should the hero use a scannable TL;DR narrative (what we research, current system, model goal) instead of a single long paragraph?","why":"Executives and researchers often scan pages quickly; unclear first-glance structure reduces comprehension and trust.","defaultAssumption":"Keep a single marketing paragraph with generic CTA links.","answer":"Use a scannable TL;DR hero structure with concise evidence-oriented cards, objective language, and direct pathway CTAs."},{"id":"Q18","question":"Should repository routes continue exposing internal operational logs like `CHANGELOG.md` in the public frontend?","why":"Public operational logs add noise for executive audiences and may expose internal implementation churn not needed for decision review.","defaultAssumption":"Keep broad route allowlist for convenience.","answer":"Do not expose `CHANGELOG.md` on frontend routes; keep public repo links restricted to research artifacts, reports, summaries, and approved briefing documents."},{"id":"Q19","question":"Should the neural-network line (MLP and next tabular-DL variants) replace current tree/tabular anchors immediately after v9?","why":"Early promotion can increase model-risk if apparent gains are not stable across larger external cohorts.","defaultAssumption":"Keep neural models as active challengers while tabular anchors remain primary for external holdout stability.","answer":"Continue neural-network research aggressively (v9 and next challengers like TabM-style retrieval/ensemble variants), but keep v8/v7 operational anchors until larger disjoint holdouts show statistically credible lift."},{"id":"Q20","question":"Should the next continuation pass train a soft-voting challenger that blends neural and tabular learners, or keep only single-model experiments?","why":"A blended challenger can improve stability on small tabular cohorts but adds model-composition complexity and governance overhead.","defaultAssumption":"Train a blended soft-voting challenger as research-only, then compare it head-to-head against v9/v8/v7/v6/v4 on set11.","answer":"Proceed with a v10 soft-voting challenger (MLP + RF + logistic components) as an R&D candidate only; do not promote it operationally unless larger disjoint holdouts show credible lift."},{"id":"Q21","question":"Should probability calibration become a required step for blended challengers in the next neural/tabular continuation cycle?","why":"Calibration can improve probability reliability and threshold interpretability, but may add variance on very small datasets.","defaultAssumption":"Run calibrated challengers as a parallel research track (not a mandatory replacement), and preserve uncalibrated anchors for direct comparability.","answer":"Yes, include calibrated challengers in every continuation cycle as a mandatory comparator track, but keep deployment anchors unchanged until calibration demonstrates robust lift on larger disjoint holdouts."},{"id":"Q22","question":"Should the frontend continue exposing pass-by-pass browsing in primary pages, or switch to a latest/combined findings-first presentation?","why":"Executive audiences can misread long pass catalogs as fragmentation instead of progress; combined views improve decision clarity.","defaultAssumption":"Keep pass archives in the repository but show only latest and combined findings in primary frontend routes.","answer":"Switch primary frontend pages to latest/combined findings-first presentation and remove pass-by-pass browsing from prominent surfaces."},{"id":"Q23","question":"Should we provision GPU now for the next continuation cycle, or keep CPU-first execution until heavier neural candidates are introduced?","why":"Early GPU setup improves readiness for larger neural sweeps, but current calibrated/tabular and small-holdout significance loops are CPU-manageable.","defaultAssumption":"Keep CPU-first for immediate set12 continuation, then request GPU when launching larger neural challenger families.","answer":"Keep CPU-first right now; request GPU setup before the next high-compute neural training wave (for example broader MLP ensembles or tabular foundation-model challengers)."},{"id":"Q24","question":"Should new external holdouts prioritize high-separation confidence sampling (faster signal) or include boundary-randomized sampling (harder but less optimistic)?","why":"Confidence-filtered cohorts can inflate apparent generalization and understate deployment risk; boundary-focused cohorts better stress decision thresholds.","defaultAssumption":"Use high-separation cohorts for rapid expansion but pair them with boundary-randomized cohorts before any model-promotion action.","answer":"Keep the current high-separation set13 as a supplementary slice and make the next cohort boundary-randomized (set14) for harder generalization testing."},{"id":"Q25","question":"For boundary-focused cohorts with assumption-driven labels, should they be treated as primary promotion evidence or as stress-test evidence only?","why":"Boundary cohorts are useful for failure-mode detection but can carry higher label uncertainty, especially when sourced from broader neurologic gene spaces.","defaultAssumption":"Treat set14-class cohorts as stress-test evidence that can block promotion, but not as standalone evidence to approve promotion.","answer":"Keep set14 as a promotion-gating stress slice and require concordant improvement across both boundary and clean external cohorts before any model-status upgrade."},{"id":"Q26","question":"If a new external cohort (set15) shows perfect separation across all comparator models, should that trigger promotion readiness?","why":"Ceiling-level results on small assumption-driven cohorts can reflect optimistic sampling and hide boundary failures seen in harder slices.","defaultAssumption":"Classify set15-like perfect-separation slices as consistency checks only and require harder boundary-randomized confirmation before promotion.","answer":"Do not treat set15 as promotion evidence; keep it as optimistic consistency-only evidence and prioritize a harder set16 boundary cohort for gating."},{"id":"Q27","question":"If both set15 and harder set16 still show ceiling-level parity, should we upgrade model status anyway?","why":"Repeated ceiling cohorts may still fail to expose boundary failures if candidate selection remains overly separable.","defaultAssumption":"Keep promotion blocked and force next cohort design toward lower-confidence positives plus higher-risk disjoint controls.","answer":"Keep status unchanged (research-only comparator) and move to set17 with deliberate low-confidence boundary targeting before any promotion decision."},{"id":"Q28","question":"If set17 breaks ceiling-level parity but v11 still ties with v8/v7/v6/v4, should we promote v11 or keep anchor status unchanged?","why":"Partial lift (vs v10/v9 only) may indicate progress without sufficient evidence for operational upgrade over current anchors.","defaultAssumption":"Keep promotion blocked and require a larger adjudicated set18 to test separation against v8/v7/v6/v4 ties.","answer":"Keep status unchanged (research-only comparator) and proceed to set18 with adjudicated boundary labels plus larger disjoint size before any promotion decision."},{"id":"Q29","question":"If larger adjudicated set18 shows v11 degradation and v4 re-emerges as best external performer, should the program revert comparator priority to v4-class anchors?","why":"Promotion and comparator governance should follow external holdout behavior, not internal preference for newer blended models.","defaultAssumption":"Keep v11 as research-only challenger and re-anchor external gating decisions on strongest stable performer while continuing challenger research.","answer":"Yes. Keep v11 in active R&D, but revert external gating priority to v4/v6-class anchors until new challengers repeatedly outperform them on larger disjoint adjudicated cohorts."},{"id":"Q30","question":"If expanded adjudicated set19 still does not place v11 as external best model at threshold 0.38, should we continue neural/challenger optimization while keeping anchor-first governance and requiring an additional replication cohort?","why":"Single-cohort improvements or ties can misstate robustness; promotion governance should require repeated superiority on independent disjoint cohorts.","defaultAssumption":"Keep v11 as research-only, retain anchor-first external gating, and proceed to set20 replication before any promotion consideration.","answer":"Yes. Continue challenger research aggressively, but require another disjoint adjudicated replication cohort (set20) before changing model-governance status."},{"id":"Q31","question":"Should executive-facing frontend pages hide internal question logs and multi-pass artifact lists by default, while still keeping those files in the repository for audit?","why":"Internal process logs and long pass lists can dilute decision clarity for executive reviewers and increase accidental exposure of operational churn.","defaultAssumption":"Keep only latest/combined findings links on primary pages and remove internal question tracking panels from executive routes.","answer":"Yes. Keep executive surfaces focused on latest combined findings and current external holdout evidence, while preserving full question/pass logs in the repository for audit."},{"id":"Q32","question":"With set21 now completed and showing v4 as best external performer at threshold 0.38, should governance continue anchor-first while v11 remains an active challenger?","why":"set21 is a low-confidence/hard-control stress cohort; governance policy must distinguish challenger progress from promotion evidence.","defaultAssumption":"Keep anchor-first external gating, classify v11 as research-only comparator, and continue with another disjoint replication cohort before promotion review.","answer":"Yes. Keep anchor-first governance and v11 as an active research-only challenger until repeated independent cohorts show stable superiority."},{"id":"Q33","question":"After set22 shows near-ceiling separability on policy metrics but still keeps v4 as external-best model, should we tighten cohort-design constraints to reduce ceiling bias before any governance change?","why":"Ceiling-like cohorts can overstate readiness and reduce sensitivity to boundary failures that matter for real external deployment behavior.","defaultAssumption":"Keep anchor-first governance unchanged and design set23 with stronger anti-ceiling constraints (lower-confidence positives and denser hard-control edge cases).","answer":"Yes. Keep governance unchanged and force anti-ceiling design in set23 before any model-promotion discussion."},{"id":"Q34","question":"After set23 anti-ceiling holdout shows wider v11 degradation versus anchor models and expanded residual false negatives, should we keep anchor-first governance and prioritize targeted boundary-signal research before any promotion review?","why":"set23 is designed to reduce optimistic ceiling bias; if v11 regresses here, promotion risk is higher unless boundary behavior improves on independent cohorts.","defaultAssumption":"Keep anchor-first governance, keep v11 as research-only challenger, and move to set24 with targeted low-confidence positives and hard-control edge cases focused on current v11 false-negative clusters.","answer":"Yes. Keep anchor-first governance unchanged and prioritize boundary-focused set24 continuation plus targeted challenger adjustments before any promotion discussion."},{"id":"Q35","question":"After set24 boundary-continuation holdout shows v11 below v6/v8/v7/v4 anchors and expands residual false negatives, should we keep anchor-first governance and move to a set25 replication before any challenger-promotion review?","why":"set24 is a deliberately hard, fresh disjoint cohort; v11 underperformance here means promotion would overfit easier validation slices.","defaultAssumption":"Keep anchor-first governance, keep v11 as research-only challenger, and construct set25 with fresh adjudicated low-confidence positives plus hard controls to replicate set24 behavior.","answer":"Assumed yes under standing instruction to continue with calculated answers; keep governance unchanged and prioritize set25 replication before any model-status change."},{"id":"Q36","question":"After set25 boundary-replication holdout confirms v11 underperformance versus v4/v8/v7/v6 anchors, should governance remain anchor-first while the frontend shifts to the shared dark editorial reference design?","why":"The model evidence and executive presentation now point in the same direction: avoid promotion claims, make latest external validation obvious, and visually separate the platform from generic dashboard styling.","defaultAssumption":"Keep v11 research-only, keep v4/v8/v7/v6 as external-gating anchors, proceed to set26 boundary replication, and apply the dark editorial frontend reference as the overview design direction.","answer":"Assumed yes under standing instruction to continue with calculated answers; ship the dark editorial refresh and keep model governance unchanged."},{"id":"Q37","question":"After set26 shows v11 recovering versus v10/v9 but still below v4/v8 anchors at threshold 0.38, should governance remain anchor-first and proceed to set27 replication before any status review?","why":"set26 is harder and shows some challenger recovery, but promotion needs repeated superiority over anchor models, not only improvement over recent challengers.","defaultAssumption":"Keep v11 research-only, keep v4/v8 as external-gating anchors, and construct set27 with fresh adjudicated low-confidence positives plus hard controls before any model-status change.","answer":"Assumed yes under standing instruction to continue with calculated answers; governance remains unchanged and set27 is the next continuation target."}]}