# Rubric10-Semantic Evaluation Summary Report
# Comparison of 4 Bridge2AI Projects (claudecode_agent method)

**Evaluation Date:** 2025-12-20  
**Rubric:** rubric10-semantic (10 elements, 50 sub-elements, binary 0/1 scoring)  
**Method:** claudecode_agent (deterministic, temperature=0.0)  
**Model:** claude-sonnet-4-5-20250929

## Overall Performance Comparison

| Project | Total Score | Percentage | Grade | Rank |
|---------|-------------|------------|-------|------|
| **VOICE** | 46/50 | 92.0% | A- | 1 |
| **CM4AI** | 42/50 | 84.0% | B | 2 |
| **AI_READI** | 37/50 | 74.0% | C+ | 3 |
| **CHORUS** | 28/50 | 56.0% | D+ | 4 |

**Average Score:** 38.25/50 (76.5%)  
**Score Range:** 18 points (56%-92%)

## Element-Level Performance

### Perfect Scores by Element

**Element 7: Scientific Motivation and Funding Transparency (100% across ALL projects)**
- All 4 projects achieved perfect 5/5 scores
- Demonstrates excellent grant documentation, purposes, tasks, and creator attribution
- NIH grant numbers follow proper format across all projects

**Element 8: Technical Transparency (75% of projects)**
- 3/4 projects achieved perfect 5/5 scores (VOICE, AI_READI, CM4AI)
- CHORUS: 4/5 (strong but missing software_and_tools field)

### Weakest Elements Across All Projects

**Element 9: Dataset Evaluation and Limitations Disclosure**
- Average: 2.75/5 (55%)
- Best: VOICE, AI_READI, CM4AI at 3/5 (60%)
- Worst: CHORUS at 2/5 (40%)
- **Critical gap:** All projects missing explicit known_limitations and known_biases fields

**Element 6: Data Provenance and Version Tracking**
- Average: 3.0/5 (60%)
- Best: VOICE at 5/5 (100%) with 4 documented versions
- Worst: CM4AI, CHORUS at 2/5 (40%)
- **Critical gap:** Only 1/4 projects has formal version tracking

## Key Findings

### Common Strengths

1. **Perfect scientific motivation and funding transparency** (4/4 projects)
2. **Strong technical transparency** (3.75/5 average)
3. **Good data composition documentation** (4.5/5 average)
4. **Comprehensive ethical frameworks** (3.75/5 average)

### Common Weaknesses

1. **Limitations and risk disclosure** (2.75/5 average)
   - Missing known_limitations field (4/4 projects)
   - Missing known_biases field (4/4 projects)

2. **Variable-level metadata** (1/4 projects complete)
   - Only VOICE has comprehensive variables field
   - Critical gap for data reuse and interoperability

3. **Version tracking infrastructure** (1/4 projects complete)
   - Only VOICE has formal version tracking
   - Others missing version, version_access fields

4. **Standards conformance documentation** (1/4 projects)
   - Only VOICE has conforms_to field
   - Despite extensive standards usage (OMOP, DICOM, FHIR)

## Project-Specific Summaries

### VOICE (92%, Rank 1) - Gold Standard
**Perfect scores in 8/10 elements**

**Strengths:**
- Complete DOI (10.13026/ns8x-gg94)
- 45 keywords for discoverability
- Multi-layered privacy (HIPAA Safe Harbor, Certificate of Confidentiality)
- 4 versions fully documented (v1.0, v1.1, v2.0.0, v2.0.1)
- 7 preprocessing strategies with technical specs
- Complete variable metadata

**Weaknesses:**
- Missing explicit known_biases field
- No typed related_datasets to other Bridge2AI projects

**Recommendation:** Add structured bias and limitations fields to achieve 50/50.

### CM4AI (84%, Rank 2) - Excellent Technical Documentation
**Perfect scores in 6/10 elements**

**Strengths:**
- 12 external resources (best cross-platform integration)
- Comprehensive multimodal documentation (imaging/mass spec/sequencing)
- $5.3M FY2025 budget transparency
- Non-human subjects ethics thoroughly documented
- 15 creators with detailed roles

**Weaknesses:**
- Weak version tracking (2/5)
- Missing variables field
- No format/encoding fields

**Recommendation:** Add version tracking infrastructure and variable-level metadata.

### AI_READI (74%, Rank 3) - Strong Technical Depth
**Perfect scores in 2/10 elements**

**Strengths:**
- Perfect scientific motivation (100%)
- Perfect technical transparency (100%)
- 12 acquisition methods (most comprehensive)
- Triple-balanced sampling for health equity
- 4,000-participant scale

**Weaknesses:**
- DOI only in external_resources (not top-level field)
- No variable metadata despite 10+ data domains
- Missing standards conformance
- Poor cross-platform integration (2/5)

**Recommendation:** Elevate DOI to top level, add variables and conforms_to fields.

### CHORUS (56%, Rank 4) - Rich Narratives, Weak Structure
**Perfect scores in 1/10 element**

**Strengths:**
- Perfect scientific motivation (100%)
- 238-word comprehensive description
- 19 creators with roles
- Multi-modal standardization (OMOP, DICOM, WFDB, OHNLP, EDF+)
- $5.88M funding fully documented

**Weaknesses:**
- Lowest overall score (56%)
- No DOI
- Weak structured ethics fields (1/5) despite strong narrative
- Poor cross-platform integration (1/5)
- Many fields documented narratively but not in schema

**Recommendation:** Convert narrative documentation to structured schema fields, add DOI.

## Critical Recommendations for All Projects

### Immediate Priority

1. **Add known_limitations field** (4/4 projects)
2. **Add known_biases field** (4/4 projects)
3. **Add variable-level metadata** (3/4 projects)

### High Priority

4. **Add standards conformance** (3/4 projects) - populate conforms_to
5. **Improve version tracking** (3/4 projects) - add version, version_access
6. **Add cross-dataset relationships** (3/4 projects) - use related_datasets

### Medium Priority

7. **Elevate DOI to top level** (2/4 projects)
8. **Add publisher field** (3/4 projects)
9. **Add citation field** (3/4 projects)

## Semantic Analysis Highlights

### Correctness Validations

**DOI Format:** All DOIs valid (PhysioNet, Zenodo, Dataverse prefixes)
**Grant Numbers:** All NIH grant numbers follow proper format
**Internal Consistency:** Strong across all projects

### Consistency Checks Summary

| Project | Passed | Failed | Warnings |
|---------|--------|--------|----------|
| VOICE | 18 | 0 | 2 |
| CM4AI | 15 | 2 | 3 |
| AI_READI | 12 | 7 | 5 |
| CHORUS | 10 | 8 | 6 |

## Conclusion

The evaluation reveals **significant variation** in D4D datasheet completeness (56%-92% range):

1. **VOICE sets the gold standard** at 92% with exemplary structured documentation
2. **All projects excel at scientific motivation** (100% across all)
3. **Common gaps exist in limitations disclosure and variable metadata**
4. **Trade-off between narrative richness and structured compliance** (CHORUS vs. VOICE)

**Key Insight:** Projects prioritizing structured schema compliance (VOICE, CM4AI) score higher than those favoring narrative documentation (CHORUS). The optimal approach combines rich narratives AND structured schema fields for both human-readability and machine-readability.

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**Evaluation Files:**
- `data/evaluation_llm/rubric10_semantic/concatenated/AI_READI_claudecode_agent_evaluation.json`
- `data/evaluation_llm/rubric10_semantic/concatenated/CHORUS_claudecode_agent_evaluation.json`
- `data/evaluation_llm/rubric10_semantic/concatenated/CM4AI_claudecode_agent_evaluation.json`
- `data/evaluation_llm/rubric10_semantic/concatenated/VOICE_claudecode_agent_evaluation.json`

**D4D Files Evaluated:**
- `data/d4d_concatenated/claudecode_agent/AI_READI_d4d.yaml` (717 lines, Dec 20 00:06)
- `data/d4d_concatenated/claudecode_agent/CHORUS_d4d.yaml` (769 lines, Dec 20 00:10)
- `data/d4d_concatenated/claudecode_agent/CM4AI_d4d.yaml` (777 lines, Dec 20 00:11)
- `data/d4d_concatenated/claudecode_agent/VOICE_d4d.yaml` (765 lines, Dec 20 00:11)

**Report Generated:** 2025-12-20T17:41:00Z
