# Rubric10-Semantic Evaluation Regeneration Summary Report

**Date**: December 25, 2024
**Scope**: All 4 Bridge2AI projects (AI_READI, CHORUS, CM4AI, VOICE)
**Method**: claudecode_agent (deterministic temperature=0.0)
**Rubric**: rubric10-semantic (10 elements, 50 sub-elements, binary scoring)

---

## Executive Summary

Successfully regenerated all 4 rubric10-semantic evaluations with schema compliance, replacing the previous inconsistent structures. All evaluations now conform to `rubric10_semantic_schema.json`, passed validation, and generated HTML successfully.

### Overall Scores

| Project | Score | Percentage | Rank |
|---------|-------|------------|------|
| **CM4AI** | **48/50** | **96%** | 🥇 1st |
| **VOICE** | **44/50** | **88%** | 🥈 2nd |
| **AI_READI** | **42/50** | **84%** | 🥉 3rd |
| **CHORUS** | **38/50** | **76%** | 4th |
| **Average** | **43/50** | **86%** | - |

---

## Detailed Element Breakdown

### Element Performance Across Projects

| Element | AI_READI | CHORUS | CM4AI | VOICE | Avg |
|---------|----------|--------|-------|-------|-----|
| 1. Discovery & Identification | 3/5 | 4/5 | 4/5 | 4/5 | 3.75 |
| 2. Access & Retrieval | 5/5 | 4/5 | 5/5 | 5/5 | 4.75 |
| 3. Reuse & Interoperability | 4/5 | 4/5 | 5/5 | 4/5 | 4.25 |
| 4. Ethics & Privacy | 5/5 | 3/5 | 5/5 | 5/5 | 4.50 |
| 5. Composition & Structure | 4/5 | 4/5 | 5/5 | 5/5 | 4.50 |
| 6. Provenance & Versioning | 4/5 | 3/5 | 5/5 | 5/5 | 4.25 |
| 7. Motivation & Funding | 5/5 | 5/5 | 5/5 | 5/5 | 5.00 |
| 8. Technical Transparency | 5/5 | 5/5 | 5/5 | 5/5 | 5.00 |
| 9. Limitations Disclosure | 2/5 | 2/5 | 4/5 | 2/5 | 2.50 |
| 10. Community Integration | 5/5 | 4/5 | 5/5 | 4/5 | 4.50 |
| **TOTAL** | **42/50** | **38/50** | **48/50** | **44/50** | **43/50** |

### Perfect Scores (5/5)

**Elements where ALL projects scored perfectly:**
- Element 7: Scientific Motivation and Funding Transparency (5/5 across all projects)
- Element 8: Technical Transparency - Data Collection and Processing (5/5 across all projects)

### Weakest Elements

**Element 9 (Limitations Disclosure)** - Average: 2.50/5 (50%)
- AI_READI: 2/5
- CHORUS: 2/5
- VOICE: 2/5
- CM4AI: 4/5 (exception - has better limitations documentation)

**Common missing components:**
- `known_limitations` field not populated
- `known_biases` field not populated
- `anomalies` field not populated

---

## Individual Project Highlights

### CM4AI - 48/50 (96%) 🥇

**Strengths:**
- Only 2 points lost (both in Element 1 and Element 9)
- Perfect scores (5/5) in 8 out of 10 elements
- Exceptional across Access, Ethics, Composition, Provenance, Motivation, Technical, Integration
- Comprehensive standardization (OMOP, DICOM, WFDB, OHNLP)
- Strong external resources and documentation

**Weaknesses:**
- Element 1: Missing hierarchical dataset relationships
- Element 9: Missing explicit bias documentation

### VOICE - 44/50 (88%) 🥈

**Strengths:**
- Perfect scores (5/5) in 6 elements
- Outstanding ethics documentation (IRB, consent, de-identification, Certificate of Confidentiality)
- Comprehensive versioning history (v1.0, v1.1, v2.0.0, v2.0.1)
- Extensive preprocessing pipeline (7 strategies) and cleaning (3 strategies)
- Strong funding transparency with detailed grant information

**Weaknesses:**
- Element 1: No hierarchical structure (parent_datasets, related_datasets)
- Element 3: Missing schema conformance statements
- Element 9: No explicit limitations/biases/anomalies documentation

### AI_READI - 42/50 (84%) 🥉

**Strengths:**
- Perfect scores (5/5) in 5 elements
- Strong ethics and access documentation
- Comprehensive preprocessing (10 strategies) and cleaning (4 strategies)
- Detailed funding information and creator attribution
- Extensive external resources

**Weaknesses:**
- Element 1: Missing DOI, hierarchical relationships (3/5)
- Element 3: Missing schema conformance (4/5)
- Element 5: Missing anomalies documentation (4/5)
- Element 6: Missing provenance fields (4/5)
- Element 9: No limitations/biases documentation (2/5)

### CHORUS - 38/50 (76%)

**Strengths:**
- Perfect scores (5/5) in 2 elements (Motivation, Technical)
- Exceptional technical transparency with extensive collection/acquisition/preprocessing
- Strong multi-modal standardization
- Comprehensive ethics oversight (14 IRBs)
- Extensive external resources (28 GitHub repositories)

**Weaknesses:**
- Element 1: No DOI/RRID (uses URL identifier) (4/5)
- Element 4: Consent procedures unclear for retrospective collection (3/5)
- Element 6: No version tracking despite continuous updates (3/5)
- Element 9: No limitations/biases/anomalies documentation (2/5)
- Element 10: Missing citation and some integration features (4/5)

---

## Common Patterns

### Universal Strengths

1. **Scientific Motivation (Element 7)**: All projects scored 5/5
   - Clear purposes and tasks
   - Comprehensive funding information
   - Strong creator attribution

2. **Technical Transparency (Element 8)**: All projects scored 5/5
   - Detailed collection mechanisms
   - Comprehensive preprocessing strategies
   - Well-documented software and tools

3. **Access & Retrieval (Element 2)**: Average 4.75/5
   - Clear access policies
   - Multiple distribution formats
   - Comprehensive external resources

### Universal Weaknesses

1. **Limitations Disclosure (Element 9)**: Average 2.50/5
   - `known_limitations` field not populated
   - `known_biases` field not populated
   - `anomalies` field not populated
   - Only CM4AI has better documentation (4/5)

2. **Optional Schema Fields**: Many fields unused despite relevant information
   - Fields documented in narratives rather than structured schema fields
   - Examples: `version`, `publisher`, `citation`, `conforms_to`, `variables`, `is_tabular`

3. **Hierarchical Structure (Element 1 component)**: Missing in all projects
   - `parent_datasets` not populated
   - `related_datasets` not populated
   - No typed relationships to other Bridge2AI datasets

---

## Semantic Analysis Insights

### Issues Detected (Common Across Projects)

1. **Missing Schema Fields** (info severity)
   - Many optional D4D schema fields not populated
   - Information exists in narratives but not in structured fields
   - Affects machine-readability and semantic interoperability

2. **Narrative vs. Structured** (info severity)
   - Important information in narrative fields (`human_subject_research`, `updates`, `preprocessing_strategies`)
   - Not extracted into dedicated schema fields when available

3. **Limitations Documentation** (warning severity)
   - Dataset limitations, biases, anomalies not explicitly documented
   - Sampling characteristics mentioned but not formalized

### Consistency Checks (Typical Results)

**Passed:**
- DOI format validation
- Grant format validation
- URL validity
- Chronological version history
- Funding matches NIH RePORTER
- Creator attribution completeness

**Warnings:**
- Schema fields underutilized
- Limitations not explicit
- Related datasets not linked
- Missing RRID identifiers

---

## Comparison to Rubric20-Semantic Results

### Rubric20 (20 questions, 84 max points)

| Project | Rubric20 Score | Percentage |
|---------|----------------|------------|
| VOICE | 84/84 | 100% 🏆 |
| AI_READI | 82/84 | 97.6% |
| CM4AI | 82/84 | 97.6% |
| CHORUS | 78/84 | 92.9% |
| **Average** | **81.5/84** | **97.0%** |

### Rubric10 (10 elements, 50 max points)

| Project | Rubric10 Score | Percentage |
|---------|----------------|------------|
| CM4AI | 48/50 | 96% 🏆 |
| VOICE | 44/50 | 88% |
| AI_READI | 42/50 | 84% |
| CHORUS | 38/50 | 76% |
| **Average** | **43/50** | **86%** |

### Key Differences

1. **Ranking Change**: VOICE dropped from 1st (Rubric20: 100%) to 2nd (Rubric10: 88%)
2. **CM4AI Rise**: CM4AI rose from tied 2nd (Rubric20: 97.6%) to 1st (Rubric10: 96%)
3. **Stricter Scoring**: Rubric10 averages 86% vs Rubric20 averages 97% (11% difference)
4. **Different Focus**: Rubric10 emphasizes hierarchical structure, limitations disclosure, and schema field utilization more heavily

### Why Different Results?

**Rubric20 focuses on:**
- Structural completeness
- Metadata quality
- Technical documentation
- FAIRness principles

**Rubric10 focuses on:**
- Schema-specific field utilization
- Hierarchical relationships
- Explicit limitations documentation
- Semantic consistency

**Rubric10 is stricter on:**
- Optional schema fields (penalizes unused fields even with narrative documentation)
- Explicit limitations/biases/anomalies (requires dedicated fields)
- Hierarchical structure (parent_datasets, related_datasets)

---

## Recommendations

### High Priority (Apply to All Projects)

1. **Add Explicit Limitations Documentation**
   ```yaml
   known_limitations:
     - id: limitation-001
       description: Targeted recruitment introduces selection bias
       impact: Results may not generalize to broader populations
   ```

2. **Document Known Biases**
   ```yaml
   known_biases:
     - id: bias-001
       description: Demographic selection bias from clinic-based recruitment
       mitigation: Stratified sampling across multiple sites
   ```

3. **Link Related Datasets**
   ```yaml
   related_datasets:
     - id: link-001
       dataset_id: https://bridge2ai.org/datasets/ai-readi
       relationship_type: sibling_project
       description: Part of Bridge2AI consortium
   ```

### Medium Priority

4. **Populate Version Fields**
   ```yaml
   version: "2.0.1"
   version_access:
     id: version-access-001
     version_url: https://doi.org/10.13026/version-specific
   ```

5. **Add Schema Conformance**
   ```yaml
   conforms_to:
     - https://w3id.org/dcat
     - https://schema.org/Dataset
     - https://bridge2ai.org/data-sheets-schema
   ```

6. **Create Formatted Citations**
   ```yaml
   citation: >
     LastName, FirstName, et al. (2025). Dataset Title. Publisher.
     https://doi.org/10.xxxxx/xxxxx
   ```

### Low Priority

7. **Add Publisher Field**
   ```yaml
   publisher: PhysioNet / MIT Laboratory for Computational Physiology
   ```

8. **Add RRID When Available**
   ```yaml
   rrid: RRID:SCR_021901
   ```

9. **Document Anomalies**
   ```yaml
   anomalies:
     - id: anomaly-001
       description: Missing data for 3% of participants
       handling: Imputed using median values
   ```

---

## Technical Workflow Summary

### Tools and Scripts Used

1. **Schema**: `src/download/prompts/rubric10_semantic_schema.json`
2. **Rubric**: `data/rubric/rubric10.txt`
3. **Fix Script**: `scripts/fix_evaluation_scores.py` (supports both rubric10 and rubric20)
4. **Validation**: `scripts/validate_evaluation_schema.py` (updated to load rubric10 schema)
5. **HTML Renderer**: `scripts/render_evaluation_html_rubric10_semantic.py`

### Workflow Executed

```bash
# 1. Generate evaluations (parallel agents)
#    - AI_READI_claudecode_agent_evaluation.json
#    - CHORUS_claudecode_agent_evaluation.json
#    - CM4AI_claudecode_agent_evaluation.json
#    - VOICE_claudecode_agent_evaluation.json

# 2. Fix scores (recalculate from sub-elements)
poetry run python scripts/fix_evaluation_scores.py \
  --input-dir data/evaluation_llm/rubric10_semantic/concatenated

# 3. Validate against schema
poetry run python scripts/validate_evaluation_schema.py
# Result: 4/4 claudecode_agent evaluations passed ✅

# 4. Generate HTML
poetry run python scripts/render_evaluation_html_rubric10_semantic.py
# Result: 4/4 HTML files generated ✅
```

### Files Generated

**JSON Evaluations:**
- `data/evaluation_llm/rubric10_semantic/concatenated/AI_READI_claudecode_agent_evaluation.json` (49KB)
- `data/evaluation_llm/rubric10_semantic/concatenated/CHORUS_claudecode_agent_evaluation.json` (48KB)
- `data/evaluation_llm/rubric10_semantic/concatenated/CM4AI_claudecode_agent_evaluation.json` (41KB)
- `data/evaluation_llm/rubric10_semantic/concatenated/VOICE_claudecode_agent_evaluation.json` (31KB)

**HTML Renderings:**
- `data/d4d_html/concatenated/claudecode_agent/AI_READI_evaluation.html` (55KB)
- `data/d4d_html/concatenated/claudecode_agent/CHORUS_evaluation.html` (50KB)
- `data/d4d_html/concatenated/claudecode_agent/CM4AI_evaluation.html` (50KB)
- `data/d4d_html/concatenated/claudecode_agent/VOICE_evaluation.html` (44KB)

---

## Quality Assurance Checklist

- [x] All 4 JSON files conform to `rubric10_semantic_schema.json`
- [x] `fix_evaluation_scores.py` completed successfully
- [x] All 4 files passed schema validation
- [x] HTML files generated without errors
- [x] HTML displays correct scores (matching `summary_scores.total_score`)
- [x] Summary report created with all sections
- [x] No "null" values in required fields
- [x] All sub-element scores are 0 or 1 (binary)
- [x] `element_score` equals sum of sub-element scores
- [x] `summary_scores.total_score` equals sum of all element scores

---

## Comparison to Previous Rubric10 Files

### Before Regeneration (Old Files)

**Structural Chaos:**
- 16 different JSON structures across 16 files
- Only 2/16 files (12.5%) had correct structure
- Field naming inconsistencies:
  - `overall_score` vs `overall_scores` vs `overall_summary` vs `overall_assessment`
  - `element_scores` (list) vs `element_scores` (dict) vs `element_evaluations` vs `detailed_scores`
- Missing required schema fields (rubric, version, project, method, timestamp, model)
- Cannot be rendered by HTML generator
- Fix script could only process 2/16 files

**Errors Found:**
- CHORUS: `element_scores` as dict instead of list → Script crashed
- CM4AI: Uses `element_evaluations` instead of `element_scores` → Returned 0/0
- AI_READI_gpt5: No `element_scores` field at all → Returned 0/0
- 13 other files: Various structural incompatibilities

### After Regeneration (New Files)

**Schema Compliance:**
- 4/4 files use identical structure
- Consistent field names: `summary_scores`, `element_scores`, `sub_elements`
- All required schema fields present
- All files passed validation ✅
- All files rendered HTML successfully ✅
- Fix script processed all 4 files successfully ✅

**Quality:**
- Comprehensive semantic analysis
- Detailed evidence for all 50 sub-elements
- Consistency checks and correctness validations
- Actionable recommendations (20-30 per project)

---

## Lessons Learned

1. **Schema Compliance is Critical**: Previous evaluations were unusable due to structural inconsistencies
2. **Post-Processing Required**: LLMs make math errors; fix script essential
3. **Validation Before HTML**: Schema validation catches issues early
4. **Consistent Structure Matters**: HTML renderers require exact field names
5. **Semantic Analysis Adds Value**: Issues detection and recommendations improve utility

---

## Future Improvements

1. **Automate Regeneration**: Create make targets for complete evaluation workflow
2. **Schema Enforcement**: Add pre-commit hooks to validate evaluation JSON structure
3. **Template Evolution**: Update D4D schema to include required fields currently documented in narratives
4. **Comparative Analysis**: Generate cross-rubric comparison reports automatically
5. **Version Tracking**: Track evaluation provenance (rubric version, schema version, model version)

---

## References

- **Schema**: `src/download/prompts/rubric10_semantic_schema.json`
- **Rubric**: `data/rubric/rubric10.txt`
- **Prompt**: `RUBRIC10_EVALUATION_PROMPT_FINAL.md`
- **Issues Report**: `RUBRIC10_ISSUES_REPORT.md`
- **Test Results**: `RUBRIC10_FIX_SCRIPT_TEST_RESULTS.md`
- **Methodology**: `RUBRIC10_UPDATED_PROMPT.md`

---

**Report Generated**: December 25, 2024
**Evaluation Method**: Claude Code Agent (deterministic, temperature=0.0)
**Model**: claude-sonnet-4-5-20250929
**Status**: ✅ Complete - All 4 projects evaluated, validated, and rendered
