{ "schemaVersion": "1.0.0", "ledgerThrough": "2026-08-31T01:29:51.711899+00:00", "purpose": "A public reconciliation of model configuration, specialist outputs, research gates, release outcomes, and call-receipt coverage for every preserved research run.", "defaultModel": "z-ai/glm-5.3", "modelDisplayName": "GLM-5.3", "router": "OpenRouter", "statisticalEngine": "Deterministic Python", "independenceDisclosure": "Role separation does not create independent peer review. Online roles currently share GLM-5.3 through OpenRouter; deterministic checklists are labeled separately.", "receiptDisclosure": "Exact tokens, cost, latency, and response identifiers are reported only when a preserved call ledger supplies them. Missing legacy receipts remain null.", "scopeDisclosure": "This ledger covers publication research runs with a research manifest and gate decision. Synthetic prompt-injection evaluations use their own public boundary contract, evaluation record, and per-attempt ledger; they are not research-publication runs and are not added to these denominators.", "excludedArtifactClasses": [ { "id": "prompt-injection", "reason": "Synthetic model evaluation, not a publication research run.", "evidenceHref": "/evidence-injection-evaluation.json" } ], "summary": { "runs": 5, "onlineRuns": 3, "offlineRuns": 2, "publishedStudies": 2, "rejectedByResearchGate": 2, "withheldAfterReleaseQa": 1, "onlineRoleOutputsCompleted": 13, "onlineRoleOutputsExpected": 15, "onlineRunsWithCallLedger": 0, "onlineRunsMissingLegacyCallLedger": 3 }, "generatedFrom": [ "artifacts/touchdown-regression/*/manifest.json", "artifacts/rookie-wr-hit-rates/*/manifest.json", "matching publication-decision.json, review-*.json, process-note.json, and openrouter-call-ledger.json when present", "content/published-runs.json" ], "runs": [ { "runId": "2026-08-31T011519Z-touchdown-regression-v1", "seriesId": "touchdown-regression", "title": "Touchdown Regression · 2026-08-31T011519Z-touchdown-regression-v1", "startedAt": "2026-08-31T01:15:19.163057+00:00", "completedAt": "2026-08-31T01:29:51.711899+00:00", "dataThrough": 2025, "deterministicSeed": 4444, "runMode": "openrouter_online", "router": "OpenRouter", "sourceManifestStatus": "published", "researchGate": { "passed": true, "gateVersion": "1.0", "decidedAt": "2026-08-31T01:29:51.709202+00:00", "checksPassed": 9, "checksTotal": 9, "checks": [ { "name": "all_specialist_decisions_pass", "passed": true }, { "name": "article_has_citations_and_attribution", "passed": true }, { "name": "attrition_reconciled", "passed": true }, { "name": "data_provenance_complete", "passed": true }, { "name": "limitations_disclosed", "passed": true }, { "name": "minimum_sample", "passed": true }, { "name": "no_blocking_review_findings", "passed": true }, { "name": "temporal_validation", "passed": true }, { "name": "uncertainty_reported", "passed": true } ], "blockerCount": 0, "decisionHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/publication-decision.json" }, "finalDisposition": "withheld_after_visual_qa", "publicStudy": { "published": false, "href": null }, "releaseNote": { "present": true, "disposition": "withheld_after_visual_qa", "reason": "The PNG bars used current 2025 analysis values, but two visible chart titles retained hard-coded 2024 launch values.", "href": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/process-note.json" }, "roles": [ { "role": "researcher", "label": "Researcher", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 1, "blockingCount": 0, "summary": "The executed study matches the locked preregistration with no material departures. All six required outputs exist and reconcile internally (1,078 walk-forward predictions; 649 evaluable next-season pairs plus 301 below the 40-target threshold plus 128 pending; quintile n's sum to 649). The registered design was followed: WR/TE regular-season player-seasons 2014-2025 at >=40 targets, expanding-window ridge (penalty 5.0) over the five registered opportunity features, test seasons 2018-2025 trained only on prior seasons. The registered estimand is reported as specified: expected TDs beat raw TDs by 0.31 MAE (2.34 vs 2.65, 11.7% improvement) with a 5,000-resample paired-bootstrap CI of 0.18-0.45. Every statistic and figure reference in the article matches analysis.json. Predictive claims are kept separate from causal claims, and all four registered risks (survivor bias, omitted red-zone/route context, non-causal interpretation, snapshot staleness) are disclosed in both the analysis and the article. The one warning concerns the inherent survivor-bias scope limitation, which is disclosed and therefore not blocking.", "artifactHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/review-researcher.json" }, { "role": "statistician", "label": "Statistician", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 3, "blockingCount": 0, "summary": "No blocking findings. The expanding-window walk-forward design is implemented and described without temporal leakage: expected touchdowns are out-of-sample for every 2018-2025 test season, and next-season outcomes never enter feature construction. Attrition reconciles exactly (649 evaluable + 301 below-threshold + 128 pending = 1,078 walk-forward predictions; retention 649/950 = 0.6832 as reported). Every headline number reproduces from the analysis JSON, and the claims stay inside the locked estimand (returning WR/TE seasons). Survivor conditioning, historical scope, and coarse covariates are disclosed in the article and are not overclaimed; the 'Where this can break' section must remain visible through publication.", "artifactHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/review-statistician.json" }, { "role": "adversarial_reviewer", "label": "Adversarial Reviewer", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 3, "blockingCount": 0, "summary": "I attempted to falsify the headline claim that an opportunity-based expected-touchdown baseline beats raw prior-year touchdowns at predicting next-season receiving TDs, and could not overturn it within the study's disclosed scope. The walk-forward design (training only on earlier seasons, testing 2018–2025), paired comparison on identical player-season pairs, and bootstrap interval excluding zero all support the direction of the finding, and the article's caveats about survivor bias, benchmark-vs-projection status, and data staleness are disclosed rather than hidden. Remaining concerns are scope limits a fantasy reader could over-apply, not invalidating flaws.", "artifactHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/review-adversarial_reviewer.json" }, { "role": "citation_checker", "label": "Citation Checker", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 1, "blockingCount": 0, "summary": "Every central numeric claim in the article traces to the supplied analysis; both figures named in the article appear in the SHA-256 inventory; both nflverse source URLs are present in the provenance record; the CC BY 4.0 license and underlying-rights caveat are preserved and compatible. The article accurately discloses the 2025 data-through date and avoids current-player claims, so source recency is a warning only.", "artifactHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/review-citation_checker.json" }, { "role": "editor", "label": "Editor", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 3, "blockingCount": 0, "summary": "All seven mechanical gates pass, blocking_finding_count is zero, and all four specialist reviews (researcher, statistician, adversarial reviewer, citation checker) returned pass with no required changes. Every number in the article reproduces from analysis.json, attrition reconciles exactly (649 + 301 + 128 = 1,078 walk-forward predictions; retention 649/950 = 0.6832; quintile n's sum to 649), and the walk-forward design was verified free of temporal leakage. The headline restates the maxim the study actually tests, and the body bounds it correctly as a modest benchmark improvement rather than a projection system or causal estimate; all four registered risks are disclosed in both the analysis and the article, with uncertainty reported adjacent to the relevant claims. Remaining warnings are conditions to preserve rather than changes: keep the survivor-bias and scope disclosures visible through publication, carry the caveats into email and social copy (the 3.0-TD email figure is a returning-player quintile mean), treat the unused 2025-overperformers artifacts as historical illustration if ever surfaced, confirm run-manifest completeness at publication time, and re-confirm recency language if publication slips materially beyond 2026. No concrete change is required before publication.", "artifactHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/review-editor.json" } ], "roleOutputsCompleted": 5, "roleOutputsExpected": 5, "receipts": { "status": "missing_legacy_call_ledger", "ledgerPresent": false, "ledgerHref": null, "attemptsRecorded": null, "promptTokens": null, "completionTokens": null, "totalTokens": null, "billedCostUsd": null, "latencySeconds": null, "disclosure": "This run predates the call ledger. Exact attempts, tokens, latency, response IDs, and billed cost were not preserved and are not estimated." }, "artifacts": { "inventoriedCount": 19, "manifestHref": "/research-artifacts/2026-08-31T011519Z-touchdown-regression-v1/manifest.json", "runIndexHref": "/research-artifacts/index.json" } }, { "runId": "2026-08-31T010623Z-touchdown-regression-v1", "seriesId": "touchdown-regression", "title": "Touchdown Regression · 2026-08-31T010623Z-touchdown-regression-v1", "startedAt": "2026-08-31T01:06:23.866698+00:00", "completedAt": "2026-08-31T01:09:00.635916+00:00", "dataThrough": 2025, "deterministicSeed": 4444, "runMode": "openrouter_online", "router": "OpenRouter", "sourceManifestStatus": "rejected", "researchGate": { "passed": false, "gateVersion": "1.0", "decidedAt": "2026-08-31T01:09:00.633550+00:00", "checksPassed": 7, "checksTotal": 9, "checks": [ { "name": "all_specialist_decisions_pass", "passed": false }, { "name": "article_has_citations_and_attribution", "passed": true }, { "name": "attrition_reconciled", "passed": true }, { "name": "data_provenance_complete", "passed": true }, { "name": "limitations_disclosed", "passed": true }, { "name": "minimum_sample", "passed": true }, { "name": "no_blocking_review_findings", "passed": false }, { "name": "temporal_validation", "passed": true }, { "name": "uncertainty_reported", "passed": true } ], "blockerCount": 3, "decisionHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/publication-decision.json" }, "finalDisposition": "rejected_by_research_gate", "publicStudy": { "published": false, "href": null }, "releaseNote": { "present": true, "disposition": "The candidate article was not published and no publication pull request was opened.", "reason": "The candidate article was not published and no publication pull request was opened.", "href": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/process-note.json" }, "roles": [ { "role": "researcher", "label": "Researcher", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 4, "blockingCount": 0, "summary": "The executed analysis faithfully implements the locked preregistration. Hypothesis, estimand, design, population, model class, and walk-forward test seasons (2018-2025) are unchanged from the memo. All six required outputs exist as hashed artifacts. Every statistic quoted in the article traces exactly to analysis.json (649 pairs; MAE 2.65 vs 2.34; 11.7% improvement; delta 0.31 with 95% CI 0.18-0.45; 100.0% of 5,000 resamples; quintile means 8.3 -> 5.3 and 2.9 -> 4.4). Attrition reconciles exactly: 649 evaluable + 301 below the next-season threshold + 128 pending = 1,078 walk-forward predictions, and retention 649/950 = 0.6832 matches the reported rate. Predictive claims are cleanly separated from causal claims, and all four preregistered risks are disclosed as limitations in both the analysis and the article. Four minor, non-blocking warnings remain and should be fixed in copyediting.", "artifactHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/review-researcher.json" }, { "role": "statistician", "label": "Statistician", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "revise", "warningCount": 3, "blockingCount": 0, "summary": "The walk-forward design is sound and the headline MAE improvement (2.65 → 2.34, 11.7%) is internally consistent with the reported bootstrap interval, but the article's framing overstates the evidence in three places: it presents the expected-TD baseline as beating raw TDs without acknowledging that both forecasts are weak in absolute terms, the 100% bootstrap win rate is reported without noting it is a resampling artifact of a single paired comparison rather than independent replication, and the quintile narrative implies predictive skill at the individual level that the aggregate correlation (-0.50) does not support. The survivor-bias limitation is disclosed but its direction is not: conditioning on ≥40 targets in the following season mechanically inflates apparent regression for high-TD players who are more likely to fall below the threshold. These are presentation-level issues that do not invalidate the core finding but require revision before publication.", "artifactHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/review-statistician.json" }, { "role": "adversarial_reviewer", "label": "Adversarial Reviewer", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 0, "blockingCount": 1, "summary": "...", "artifactHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/review-adversarial_reviewer.json" }, { "role": "citation_checker", "label": "Citation Checker", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "pass", "warningCount": 1, "blockingCount": 0, "summary": "All central numeric claims in the article trace to the supplied analysis, every named artifact appears in the SHA-256 inventory, both provenance records carry source URLs with CC BY 4.0 license and attribution language matching the article, and the underlying-rights caveat is preserved. The article accurately discloses the 2025 data-through date and frames examples as historical, so source recency is at most a warning, not a blocker.", "artifactHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/review-citation_checker.json" }, { "role": "editor", "label": "Editor", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "z-ai/glm-5.3", "modelIdentityAuthority": "transport_record", "decision": "revise", "warningCount": 1, "blockingCount": 0, "summary": "...", "artifactHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/review-editor.json" } ], "roleOutputsCompleted": 5, "roleOutputsExpected": 5, "receipts": { "status": "missing_legacy_call_ledger", "ledgerPresent": false, "ledgerHref": null, "attemptsRecorded": null, "promptTokens": null, "completionTokens": null, "totalTokens": null, "billedCostUsd": null, "latencySeconds": null, "disclosure": "This run predates the call ledger. Exact attempts, tokens, latency, response IDs, and billed cost were not preserved and are not estimated." }, "artifacts": { "inventoriedCount": 19, "manifestHref": "/research-artifacts/2026-08-31T010623Z-touchdown-regression-v1/manifest.json", "runIndexHref": "/research-artifacts/index.json" } }, { "runId": "2026-08-31-touchdown-regression-v1", "seriesId": "touchdown-regression", "title": "Touchdown Regression · 2026-08-31-touchdown-regression-v1", "startedAt": "2026-08-31T00:42:45.781223+00:00", "completedAt": "2026-08-31T00:46:48.618193+00:00", "dataThrough": 2024, "deterministicSeed": 4444, "runMode": "openrouter_online", "router": "OpenRouter", "sourceManifestStatus": "rejected", "researchGate": { "passed": false, "gateVersion": "1.0", "decidedAt": "2026-08-31T00:46:48.614486+00:00", "checksPassed": 6, "checksTotal": 7, "checks": [ { "name": "article_has_citations_and_attribution", "passed": true }, { "name": "data_provenance_complete", "passed": true }, { "name": "limitations_disclosed", "passed": true }, { "name": "minimum_sample", "passed": true }, { "name": "no_blocking_review_findings", "passed": false }, { "name": "temporal_validation", "passed": true }, { "name": "uncertainty_reported", "passed": true } ], "blockerCount": 13, "decisionHref": "/research-artifacts/2026-08-31-touchdown-regression-v1/publication-decision.json" }, "finalDisposition": "rejected_by_research_gate", "publicStudy": { "published": false, "href": null }, "releaseNote": { "present": true, "disposition": "The candidate article was not published and no publication pull request was opened.", "reason": "The candidate article was not published and no publication pull request was opened.", "href": "/research-artifacts/2026-08-31-touchdown-regression-v1/process-note.json" }, "roles": [ { "role": "researcher", "label": "Researcher", "status": "not_invoked", "configuredModel": "z-ai/glm-5.3", "artifactModel": null, "modelIdentityAuthority": "manifest_configured_route_artifact_metadata_non_authoritative", "decision": null, "warningCount": 0, "blockingCount": 0, "summary": null, "artifactHref": null }, { "role": "statistician", "label": "Statistician", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "ridge-regularized linear expected-touchdown model (per analysis JSON); research memo specifies OLS slope estimand with position fixed effects", "modelIdentityAuthority": "manifest_configured_route_artifact_metadata_non_authoritative", "decision": "block", "warningCount": 0, "blockingCount": 0, "summary": "The evidence bundle does not support the article's central claims. The stated estimand is an OLS slope of next-season TD rate on current-season TD rate, but the analysis instead reports quintile means and forecast-error metrics for a ridge expected-TD model; no slope estimate, confidence interval, or test of H1 (0 < beta_1 < 1) is presented. The headline '8.9% MAE improvement' is not reproducible from the reported metrics: the expected-TD MAE (2.379) vs last-year-actual MAE (2.610) gives an 8.86% improvement, but the bootstrap 95% CI for the MAE delta [0.087, 0.378] is reported in TD units while the improvement is computed on a different scale, and the claimed probability 0.9994 that expected beats actual is inconsistent with a CI whose lower bound (0.087) is close to zero relative to its width. The walk-forward evaluation covers only 2018-2024 (950 predictions) while the descriptive quintile analysis spans 2014-2024 (551 pairs), so the headline improvement and the quintile 'regression to the mean' narrative come from different samples and neither is tied to the pre-registered estimand. The 'Most under' quintile shows next-season TDs (4.29) below expected TDs (5.62), which contradicts a pure regression-to-the-mean story and is not explained. Attrition/survivorship is acknowledged as a risk but no attrition table is included in the bundle, so the selection effect conditioning on >=40 targets cannot be assessed. The forecast-error chart is referenced but no per-season breakdown is provided to verify the rolling-origin lock. The article's claims are therefore unsupported by the presented evidence.", "artifactHref": "/research-artifacts/2026-08-31-touchdown-regression-v1/review-statistician.json" }, { "role": "adversarial_reviewer", "label": "Adversarial Reviewer", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "claude-opus-4-6", "modelIdentityAuthority": "manifest_configured_route_artifact_metadata_non_authoritative", "decision": "revise", "warningCount": 4, "blockingCount": 5, "summary": "The headline claim that expected touchdowns beat last-year actuals as a next-season predictor is directionally supported by the walk-forward MAE improvement (8.9%, bootstrap P(expected better)=0.9994), but the evidence bundle contains material inconsistencies, unaddressed confounds, and scope limits that a fantasy player could easily over-apply. The bundle conflates two different studies (a quintile regression analysis and a separate research memo describing a slope-based estimand with position fixed effects), the survivorship conditioning is acknowledged but its magnitude is never quantified, and the headline comparison may be biased in favor of the model because both forecasts and the 'last-year actuals' baseline are evaluated only on players who cleared the 40-target threshold again. Revision is needed before this can be treated as a defensible, decision-grade result.", "artifactHref": "/research-artifacts/2026-08-31-touchdown-regression-v1/review-adversarial_reviewer.json" }, { "role": "citation_checker", "label": "Citation Checker", "status": "completed", "configuredModel": "z-ai/glm-5.3", "artifactModel": "claude-opus-4-1-20250805", "modelIdentityAuthority": "manifest_configured_route_artifact_metadata_non_authoritative", "decision": "block", "warningCount": 0, "blockingCount": 8, "summary": "Provenance checks fail. The bundle's summary statistics are internally consistent, but no source URL, license, attribution text, or underlying-rights caveat appears anywhere, and none of the research memo's required outputs (locked forecasts, evaluation report, attrition table, deviation log) are present as artifacts. Every central number is therefore untraceable, which is blocking under policy. 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