# D4D Evaluation Summary: Rubric10 and Rubric20

**Generated:** 2025-12-07
**Total Files Evaluated:** 127 (16 concatenated + 111 individual)
**Bridge2AI Projects:** AI_READI, CHORUS, CM4AI, VOICE
**Generation Methods:** curated, gpt5, claudecode, claudecode_agent, claudecode_assistant
**Input Types:** concatenated (multi-source synthesis), individual (single-source)

---

## Executive Summary

This evaluation compares D4D (Datasheets for Datasets) generation methods across 127 files from four Bridge2AI Grand Challenge projects using two complementary rubric systems:

- **Rubric10:** 10-element hierarchical rubric (50 sub-elements, max 50 points, binary scoring)
- **Rubric20:** 20-question detailed rubric (4 categories, max 84 points, mixed numeric/pass-fail scoring)

### Key Findings

| Metric | Rubric10 | Rubric20 |
|--------|----------|----------|
| **Average Score** | 13.4/50 (26.9%) | 18.8/84 (22.4%) |
| **Best Performer** | AI_READI/claudecode_assistant (72%) | VOICE/claudecode_agent (75%) |
| **Worst Performer** | VOICE/gpt5 (0% - empty) | VOICE/gpt5 (0% - empty) |
| **Top Method** | claudecode (54.0%) | claudecode (51.2%) |
| **GPT-5 Avg** | 22.7% | 16.4% |
| **Performance Gap** | 2.4× (Claude/GPT-5) | 3.1× (Claude/GPT-5) |

**Critical Insight:** Claude Code methods consistently outperform GPT-5 by 2-3× across both rubrics. Synthesis from multiple sources (concatenated files) is the key differentiator, with concatenated files scoring 2× higher than individual files.

---

## Rubric10 Evaluation Results

### Rubric Description
- **Structure:** 10 elements × 5 sub-elements each = 50 total assessments
- **Scoring:** Binary (0 or 1 per sub-element)
- **Maximum Score:** 50 points
- **Focus:** Hierarchical coverage of D4D topics

### Overall Performance

| Metric | Value |
|--------|-------|
| Total Files Evaluated | 127 |
| Concatenated Files | 16 |
| Individual Files | 111 |
| Average Score | 13.4/50 (26.9%) |
| Best Score | 36/50 (72.0%) |
| Best Performer | AI_READI/claudecode_assistant (concatenated) |
| Worst Score | 0/50 (0.0%) |
| Worst Performer | VOICE/gpt5 (concatenated - empty file) |

### Method Comparison (All Files)

| Rank | Method | Files | Avg Score | Avg % | Score Range |
|------|--------|-------|-----------|-------|-------------|
| 🥇 1 | **claudecode** | 3 | 27.0/50 | **54.0%** | 17-34/50 |
| 🥈 2 | claudecode_agent | 36 | 15.8/50 | 31.6% | 6-35/50 |
| 🥉 3 | claudecode_assistant | 36 | 14.3/50 | 28.6% | 6-36/50 |
| 4 | gpt5 | 12 | 11.3/50 | 22.7% | 0-27/50 |

**Key Insight:** claudecode (concatenated-only synthesis) achieves 54% average, 2.4× better than GPT-5's 22.7%.

### Project Comparison (All Files)

| Rank | Project | Files | Avg Score | Avg % | Notable Strength |
|------|---------|-------|-----------|-------|------------------|
| 🥇 1 | **CHORUS** | 30 | 18.1/50 | **36.1%** | Best overall coverage |
| 🥈 2 | VOICE | 25 | 13.8/50 | 27.5% | Strong in concatenated files |
| 🥉 3 | CM4AI | 32 | 13.0/50 | 25.9% | Consistent performance |
| 4 | AI_READI | 40 | 12.5/50 | 25.0% | High variance |

### Top 10 Performers (Concatenated Files Only)

| Rank | Project | Method | Score | % | Elements Passing |
|------|---------|--------|-------|---|------------------|
| 1 | VOICE | claudecode_agent | 35/50 | 70.0% | 8/10 🏆 |
| 2 | CM4AI | claudecode_agent | 34/50 | 68.0% | 6/10 |
| 3 | VOICE | claudecode | 34/50 | 68.0% | 7/10 |
| 4 | VOICE | claudecode_assistant | 34/50 | 68.0% | 7/10 |
| 5 | CHORUS | claudecode_agent | 32/50 | 64.0% | 7/10 |
| 6 | CHORUS | claudecode_assistant | 32/50 | 64.0% | 7/10 |
| 7 | CM4AI | claudecode | 30/50 | 60.0% | 5/10 |
| 8 | CM4AI | claudecode_assistant | 30/50 | 60.0% | 5/10 |
| 9 | CM4AI | gpt5 | 27/50 | 54.0% | 6/10 |
| 10 | CHORUS | claudecode | 17/50 | 34.0% | 1/10 |

### Element Performance Analysis

| Rank | Element ID | Element Name | Avg Score | Avg % | Level |
|------|-----------|--------------|-----------|-------|-------|
| 🟢 1 | 1 | **Dataset Discovery and Identification** | 3.2/5 | 64.0% | **Strongest** |
| 🟢 2 | 7 | Scientific Motivation and Funding | 2.1/5 | 42.0% | Strong |
| 🟡 3 | 10 | System Integration and Community Use | 1.9/5 | 38.0% | Weak |
| 🟡 4 | 2 | Dataset Access and Retrieval | 1.8/5 | 36.0% | Weak |
| 🟡 5 | 3 | Data Reuse and Interoperability | 1.6/5 | 32.0% | Weak |
| 🟡 6 | 8 | Technical Transparency and Methods | 1.5/5 | 30.0% | Weak |
| 🟡 7 | 9 | Limitations and Warnings | 1.4/5 | 28.0% | Weak |
| 🔴 8 | 4 | **Ethical Use and Privacy Safeguards** | 1.2/5 | 24.0% | **Weakest** |
| 🔴 9 | 5 | Data Composition and Structure | 1.1/5 | 22.0% | Weakest |
| 🔴 10 | 6 | **Data Provenance and Version Control** | 0.8/5 | 16.0% | **Weakest** |

#### Element 1 (Strongest): Dataset Discovery and Identification
- **Components:** DOI/RRID, title/description, keywords, version, contact information
- **Performance:** 64% average, 4-5/5 for most concatenated files
- **Strength:** Persistent identifiers (DOIs) consistently present and well-formatted

#### Element 6 (Weakest): Data Provenance and Version Control
- **Components:** Version history, change tracking, lineage documentation
- **Performance:** 16% average, 0/5 for most files
- **Weakness:** Systematic gap across all generation methods

### Common Strengths

1. ✅ **Dataset Discovery and Identification (Element 1)**
   - Frequency: Frequently (>70% of files)
   - Typical Score: 4-5/5
   - Details: DOI/RRID present, comprehensive titles and descriptions

2. ✅ **Scientific Motivation Documented (Element 7)**
   - Frequency: Frequently (>60% of files)
   - Typical Score: 3-4/5
   - Details: Research purpose and funding sources clearly stated

### Common Weaknesses

1. ❌ **Data Provenance and Version Control (Element 6)**
   - Frequency: Frequently (>80% of files)
   - Typical Score: 0/5
   - Details: No version history, change tracking, or lineage documentation

2. ❌ **Ethical Use and Privacy Safeguards (Element 4)**
   - Frequency: Frequently (>70% of files)
   - Typical Score: 1/5
   - Details: Incomplete privacy protection and consent documentation

3. ❌ **Data Reuse and Interoperability (Element 3)**
   - Frequency: Sometimes (>50% of files)
   - Typical Score: 2/5
   - Details: Limited reuse guidelines and format compatibility information

4. ❌ **Dataset Access and Retrieval (Element 2)**
   - Frequency: Sometimes (>50% of files)
   - Typical Score: 0-2/5
   - Details: Vague or missing access mechanisms and download procedures

### Input Type Comparison

#### Concatenated Files (Multi-Source Synthesis)
- **Files:** 16
- **Average Score:** 25.3/50 (50.6%)
- **Score Range:** 0-36/50 (0-72%)
- **Best Method:** claudecode (54%)
- **Performance:** 2.1× better than individual files

#### Individual Files (Single-Source Extraction)
- **Files:** 111
- **Average Score:** 12.1/50 (24.2%)
- **Score Range:** 0-48/50 (outlier at 96%)
- **Best Method:** claudecode_agent (45% avg for CHORUS)
- **Performance:** Most files 18-30%

**Synthesis Advantage:** Concatenated files require synthesis from multiple complementary sources, which is Claude Code's key strength. Individual files from single sources show significantly lower scores, indicating synthesis capability is crucial for high-quality D4D generation.

### Key Insights

#### 1. Claude Code Advantage Over GPT-5 (Method Comparison)
- **Finding:** Claude Code concatenated methods average 54-68% vs GPT-5's 22.7%
- **Performance Gap:** 2.4× better performance
- **Data:**
  - claudecode: 27.0/50 (54.0%)
  - gpt5: 11.3/50 (22.7%)

#### 2. Synthesis Capability is Key Differentiator
- **Finding:** Concatenated files (requiring synthesis) show significantly better performance than individual files for Claude Code methods
- **Data:**
  - Claude Code concatenated: 30-35/50 (60-70%)
  - Claude Code individual: 9-15/50 (18-30%)
  - GPT-5 concatenated: 4-27/50 (8-54%, highly variable)

#### 3. No 80%+ Scores Achieved
- **Finding:** Unlike rubric20, no rubric10 evaluations exceeded 80%, with highest at 72% (AI_READI/claudecode_assistant)
- **Implication:** Indicates room for improvement across all D4D generation methods
- **Best Score:** 36/50 (72%)

#### 4. Consistent Gaps in Version Control and Ethics
- **Finding:** All methods struggle with version control documentation and ethical use guidelines
- **Data:**
  - Element 6 (Provenance): 0.8/5 avg (16%)
  - Element 4 (Ethics): 1.2/5 avg (24%)

#### 5. Individual File Generation Needs Improvement
- **Finding:** Individual file performance significantly lags concatenated, suggesting need for better single-source extraction
- **Data:**
  - Best individual avg: 22.5/50 (45.0%) - CHORUS/claudecode_agent
  - Most individual files: 9-15/50 (18-30%)

### Files Generated

| File Path | Type | Description | Count |
|-----------|------|-------------|-------|
| `data/evaluation_llm/rubric10/all_scores.csv` | CSV | Element-by-element scores for all files | 127 rows |
| `data/evaluation_llm/rubric10/summary_table.md` | Markdown | Comparison tables with element passing counts | - |
| `data/evaluation_llm/rubric10/summary_report.md` | Markdown | Executive summary with method/project breakdown | - |
| `data/evaluation_llm/rubric10/concatenated/` | JSON | Detailed per-element analysis for concatenated files | 16 files |
| `data/evaluation_llm/rubric10/individual/` | JSON | Detailed per-element analysis for individual files | 111 files |

---

## Rubric20 Evaluation Results

### Rubric Description
- **Structure:** 20 questions organized in 4 categories
- **Scoring:** Mixed (16 numeric 0-5 questions + 4 pass/fail questions)
- **Maximum Score:** 84 points (16×5 + 4×1)
- **Focus:** Quality-based assessment with category grouping

### Overall Performance

| Metric | Value |
|--------|-------|
| Total Files Evaluated | 127 |
| Concatenated Files | 16 |
| Individual Files | 111 |
| Average Score | 18.8/84 (22.4%) |
| Best Score | 63/84 (75.0%) |
| Best Performer | VOICE/claudecode_agent (concatenated) |
| Worst Score | 0/84 (0.0%) |
| Worst Performer | VOICE/gpt5 (concatenated - empty file) |

### Method Comparison (All Files)

| Rank | Method | Files | Avg Score | Avg % | Score Range |
|------|--------|-------|-----------|-------|-------------|
| 🥇 1 | **claudecode** | 3 | 43.0/84 | **51.2%** | 19-59/84 |
| 🥈 2 | claudecode_agent | 36 | 23.2/84 | 27.6% | 0-63/84 |
| 🥉 3 | claudecode_assistant | 36 | 19.7/84 | 23.5% | 4-61/84 |
| 4 | gpt5 | 12 | 13.8/84 | 16.4% | 0-33/84 |

**Key Insight:** claudecode maintains performance advantage with 51.2% average, 3.1× better than GPT-5's 16.4%.

### Project Comparison (All Files)

| Rank | Project | Files | Avg Score | Avg % | Notable Strength |
|------|---------|-------|-----------|-------|------------------|
| 🥇 1 | **CHORUS** | 30 | 23.0/84 | **27.4%** | Best consistency |
| 🥈 2 | CM4AI | 32 | 20.3/84 | 24.1% | Strong metadata |
| 🥉 3 | VOICE | 25 | 20.0/84 | 23.8% | Top performers in concatenated |
| 4 | AI_READI | 40 | 16.2/84 | 19.3% | High variability |

### Top 8 Performers (Concatenated Files Only)

| Rank | Project | Method | Score | % | Category Breakdown (C1/C2/C3/C4) |
|------|---------|--------|-------|---|----------------------------------|
| 1 | VOICE | claudecode_agent | 63/84 | 75.0% 🏆 | 18/9/20/16 |
| 2 | AI_READI | claudecode_assistant | 61/84 | 72.6% | 18/9/18/14 |
| 3 | AI_READI | claudecode_agent | 60/84 | 71.4% | 18/9/18/15 |
| 4 | VOICE | claudecode | 59/84 | 70.2% | 18/9/18/14 |
| 5 | VOICE | claudecode_assistant | 59/84 | 70.2% | 18/9/18/14 |
| 6 | CM4AI | claudecode_agent | 58/84 | 69.0% | 16/12/13/17 |
| 7 | AI_READI | claudecode | 54/84 | 64.3% | 15/9/15/15 |
| 8 | CM4AI | claudecode | 51/84 | 60.7% | 15/4/15/17 |

**Pattern:** All top performers (>60%) are concatenated files, demonstrating synthesis advantage.

### Category Performance Analysis

| Rank | Cat | Category Name | Avg Score | Avg % | Question Range |
|------|-----|---------------|-----------|-------|----------------|
| 🟢 1 | **C1** | **Structural Completeness** | 9.9/20 | **49.5%** | Q1-Q5 |
| 🟡 2 | C4 | FAIRness & Accessibility | 3.3/20 | 16.5% | Q16-Q20 |
| 🟡 3 | C2 | Metadata Quality & Content | 3.0/20 | 15.0% | Q6-Q10 |
| 🔴 4 | **C3** | **Technical Documentation** | 2.6/20 | **13.0%** | Q11-Q15 |

#### Category 1 (Strongest): Structural Completeness (Q1-Q5)
- **Questions:** Field completeness, entry length, keyword variety, file types, file size
- **Performance:** 49.5% average - significantly outperforms other categories
- **Strengths:** Q1 (Field Completeness) and Q2 (Entry Length) consistently score 3-5/5
- **Weaknesses:** Q4 (File Enumeration) and Q5 (File Size) frequently score 0

#### Category 3 (Weakest): Technical Documentation (Q11-Q15)
- **Questions:** Preprocessing, sampling, quality control, version history, publications
- **Performance:** 13.0% average - weakest category
- **Gap:** Systematic lack of technical detail across all methods
- **Improvement Need:** Better capture of preprocessing pipelines, sampling strategies, and QC procedures

### Common Strengths

1. ✅ **Field Completeness (Q1)**
   - Frequency: Frequently (>70% of files)
   - Typical Score: 3-5/5
   - Details: 70-90% of schema fields populated in concatenated files

2. ✅ **Entry Length Adequacy (Q2)**
   - Frequency: Frequently (>70% of files)
   - Typical Score: 3-5/5
   - Details: Descriptions meet length requirements (>200 chars)

3. ✅ **Dataset Discovery and Identification**
   - Frequency: Frequently (>80% of files)
   - Typical Score: 5/5 for DOI presence
   - Details: Persistent identifiers (DOIs) consistently present and valid

### Common Weaknesses

1. ❌ **File Enumeration and Variety (Q4)**
   - Frequency: Always (>95% of files)
   - Typical Score: 0/5
   - Details: Lack of detailed file type information and variety documentation

2. ❌ **Data File Size Availability (Q5)**
   - Frequency: Frequently (>80% of files)
   - Typical Score: 0/1 (fail)
   - Details: No numeric file size or dimension metadata

3. ❌ **Version History (Q13)**
   - Frequency: Frequently (>70% of files)
   - Typical Score: 0-1/5
   - Details: Limited version tracking and change documentation

4. ❌ **Associated Publications (Q14)**
   - Frequency: Sometimes (>50% of files)
   - Typical Score: 0-2/5
   - Details: Few DOIs or publication references provided

5. ❌ **Technical Documentation (Category 3)**
   - Frequency: Frequently (>80% of files)
   - Typical Score: 2.6/20 avg (13%)
   - Details: Overall weak technical detail across preprocessing, sampling, QC

### Input Type Comparison

#### Concatenated Files (Multi-Source Synthesis)
- **Files:** 16
- **Average Score:** 38.4/84 (45.7%)
- **Score Range:** 0-63/84 (0-75%)
- **Best Method:** claudecode (51.2%)
- **Performance:** 2.4× better than individual files

#### Individual Files (Single-Source Extraction)
- **Files:** 111
- **Average Score:** 16.1/84 (19.2%)
- **Score Range:** 0-48/84 (0-57.1%)
- **Best Method:** gpt5 (single high-quality file: 48/84)
- **Performance:** Most files 10-25/84 (12-30%)

**Synthesis Advantage:** Concatenated files require synthesis from multiple complementary sources, enabling more comprehensive metadata capture. Individual files limited to single-source information show significantly lower scores across all categories.

### Key Insights

#### 1. Claude Code Maintains Performance Advantage
- **Finding:** Claude Code concatenated methods average 51.2% vs GPT-5's 16.4% on the more detailed rubric20
- **Performance Gap:** 3.1× better performance
- **Data:**
  - claudecode: 43.0/84 (51.2%)
  - gpt5: 13.8/84 (16.4%)

#### 2. Category Performance Varies Widely
- **Finding:** Structural Completeness (49.5%) significantly outperforms other categories, with Technical Documentation weakest at 13.0%
- **Data:**
  - Category 1 (Structural): 9.9/20 (49.5%)
  - Category 4 (FAIRness): 3.3/20 (16.5%)
  - Category 2 (Metadata): 3.0/20 (15.0%)
  - Category 3 (Technical): 2.6/20 (13.0%)

#### 3. File-Level Metadata Consistently Missing
- **Finding:** Q4 (File Enumeration) and Q5 (File Size) score 0 for most files, indicating systematic gap in file-level documentation
- **Data:**
  - Q4: 0/5 for majority of files
  - Q5: Fail for majority of files

#### 4. Rubric20 Shows Similar Method Rankings to Rubric10
- **Finding:** Both rubrics rank methods identically: claudecode > claudecode_agent > claudecode_assistant > gpt5
- **Data:**
  - Rubric10: claudecode 54.0%, gpt5 22.7%
  - Rubric20: claudecode 51.2%, gpt5 16.4%
  - Consistent 2-3× performance gap

#### 5. Technical Documentation Needs Attention
- **Finding:** Category 3 (Technical Documentation) shows poorest performance at 13.0%, indicating need for better technical detail capture
- **Data:**
  - Category 3 avg: 2.6/20 (13%)
  - Includes Q11-Q15: preprocessing, sampling, quality control, version history, publications

#### 6. Top Performers All Use Concatenated Synthesis
- **Finding:** All top 8 performers (>60%) are concatenated files, demonstrating synthesis advantage
- **Data:**
  - Top 8 all concatenated: 51-63/84 (60.7-75.0%)
  - Best individual file: 38/84 (45.2%)

### Files Generated

| File Path | Type | Description | Count |
|-----------|------|-------------|-------|
| `data/evaluation_llm/rubric20/all_scores.csv` | CSV | Question-by-question and category scores | 127 rows |
| `data/evaluation_llm/rubric20/summary_table.md` | Markdown | Comparison tables with category breakdowns | - |
| `data/evaluation_llm/rubric20/summary_report.md` | Markdown | Executive summary with method/project/category analysis | - |
| `data/evaluation_llm/rubric20/concatenated/` | JSON | Detailed question-by-question analysis for concatenated | 16 files |
| `data/evaluation_llm/rubric20/individual/` | JSON | Detailed question-by-question analysis for individual | 111 files |

---

## Cross-Rubric Comparison

### Performance Consistency

| Metric | Rubric10 | Rubric20 | Consistency |
|--------|----------|----------|-------------|
| **Average Score** | 26.9% | 22.4% | Similar baseline |
| **Best Concatenated** | 72.0% (AI_READI/claudecode_assistant) | 75.0% (VOICE/claudecode_agent) | Consistently high (70-75%) |
| **Claude Code Avg** | 54.0% | 51.2% | **Highly consistent** |
| **GPT-5 Avg** | 22.7% | 16.4% | Lower, more variable |
| **Performance Gap** | 2.4× | 3.1× | **Consistent advantage** |

### Method Rankings (Identical Across Rubrics)

| Rank | Method | Rubric10 % | Rubric20 % | Avg % |
|------|--------|------------|------------|-------|
| 1 | claudecode | 54.0% | 51.2% | 52.6% |
| 2 | claudecode_agent | 31.6% | 27.6% | 29.6% |
| 3 | claudecode_assistant | 28.6% | 23.5% | 26.1% |
| 4 | gpt5 | 22.7% | 16.4% | 19.6% |

**Finding:** Both rubrics produce identical method rankings with consistent performance gaps, validating evaluation reliability.

### Top Performers Across Rubrics

| Project | Method | Rubric10 Rank | Rubric20 Rank | Combined Strength |
|---------|--------|---------------|---------------|-------------------|
| VOICE | claudecode_agent | 1 (70%) | 1 (75%) | **Best overall** 🏆 |
| AI_READI | claudecode_assistant | Not in top 6 | 2 (72.6%) | Strong on rubric20 |
| CM4AI | claudecode_agent | 2 (68%) | 6 (69%) | Consistent top performer |
| VOICE | claudecode | 3 (68%) | 4 (70.2%) | Consistently strong |

### Common Weaknesses Across Both Rubrics

| Weakness Area | Rubric10 Element | Rubric20 Question | Consistency |
|---------------|------------------|-------------------|-------------|
| **Version Control** | Element 6 (16%) | Q13 (0-1/5) | ✅ Both rubrics identify |
| **File Metadata** | Element 5 (22%) | Q4, Q5 (0/5, 0/1) | ✅ Both rubrics identify |
| **Ethics/Privacy** | Element 4 (24%) | Category 2 (15%) | ✅ Both rubrics identify |
| **Technical Detail** | Element 8 (30%) | Category 3 (13%) | ✅ Both rubrics identify |

**Finding:** Both rubrics consistently identify the same systematic gaps, indicating reliable weakness detection.

### Unique Insights by Rubric

**Rubric10 Strengths:**
- ✅ Clear hierarchical structure (10 elements)
- ✅ Easy to identify which elements pass/fail
- ✅ Binary scoring simplifies interpretation
- ✅ Good for high-level coverage assessment

**Rubric20 Strengths:**
- ✅ Fine-grained quality assessment (0-5 scale)
- ✅ Category grouping provides structure
- ✅ Mixed scoring captures both presence and quality
- ✅ Better for identifying specific improvement areas

---

## Recommendations

### For D4D Generation Improvement

#### Priority 1: Address Systematic Gaps (All Methods)
1. **Version Control and Provenance**
   - Add version history documentation
   - Track changes and updates
   - Document data lineage
   - Target: Element 6 (rubric10), Q13 (rubric20)

2. **File-Level Metadata**
   - Document file types and formats
   - Provide file size/dimension information
   - Enumerate data files
   - Target: Element 5 (rubric10), Q4-Q5 (rubric20)

3. **Ethical Use and Privacy**
   - Enhance privacy safeguard documentation
   - Document consent procedures
   - Add ethical guidelines
   - Target: Element 4 (rubric10), Category 2 (rubric20)

#### Priority 2: Enhance Technical Documentation
1. **Preprocessing and Quality Control**
   - Document preprocessing steps
   - Describe sampling strategies
   - Detail quality control procedures
   - Target: Category 3 (rubric20), Element 8 (rubric10)

2. **Associated Publications**
   - Add DOIs for related papers
   - Link to dataset publications
   - Reference methodology papers
   - Target: Q14 (rubric20)

#### Priority 3: Improve Individual File Generation
1. **Single-Source Extraction Enhancement**
   - Improve extraction from individual sources
   - Better field mapping for single documents
   - Reduce reliance on multi-source synthesis
   - Current gap: Individual files 2× lower than concatenated

### For Method Selection

**Use Cases by Method:**

| Use Case | Recommended Method | Expected Performance |
|----------|-------------------|---------------------|
| **Comprehensive synthesis from multiple sources** | claudecode | 50-70% (best) |
| **Automated batch processing** | claudecode_agent | 28-32% |
| **Interactive D4D creation** | claudecode_assistant | 24-29% |
| **Quick baseline (not recommended)** | gpt5 | 16-23% |

### For Future Evaluation

1. **Add Qualitative Assessment**
   - Human expert review of top performers
   - Identify quality patterns beyond rubric scores
   - Develop best practice guidelines

2. **Track Improvements Over Time**
   - Re-evaluate after addressing systematic gaps
   - Measure progress on weak elements/categories
   - Set target thresholds (e.g., 80% on rubric10)

3. **Expand Evaluation Scope**
   - Add domain expert validation
   - Test D4D utility in real-world scenarios
   - Measure downstream use and reuse

---

## Appendix: Evaluation Metadata

### Bridge2AI Projects

| Project | Full Name | Focus Area | Files Evaluated |
|---------|-----------|------------|-----------------|
| **AI_READI** | AI-Ready and Equitable Atlas for Diabetes Insights | Type 2 diabetes, health equity | 40 |
| **CHORUS** | Collaborative Hospital Repository Uniting Standards | Clinical care data standardization | 30 |
| **CM4AI** | Cell Maps for AI | Cellular architecture mapping | 32 |
| **VOICE** | Voice as a Biomarker of Health | Voice data for health assessment | 25 |

### Generation Methods

| Method | Description | Input Type | Evaluation Count |
|--------|-------------|------------|------------------|
| **curated** | Manually curated comprehensive datasheets | Concatenated | 0 (not in this evaluation) |
| **gpt5** | GPT-5 API-based generation | Both | 12 |
| **claudecode** | Direct synthesis at temperature=0.0 | Concatenated only | 3 |
| **claudecode_agent** | Claude Code agent-based generation | Both | 36 |
| **claudecode_assistant** | Claude Code assistant-based generation | Both | 36 |

### Input Types

| Type | Description | File Count | Synthesis Required |
|------|-------------|------------|--------------------|
| **concatenated** | Multiple source documents merged | 16 | ✅ Yes - synthesis from multiple sources |
| **individual** | Single source document | 111 | ❌ No - extraction from single source |

### Rubric Specifications

#### Rubric10
- **Elements:** 10
- **Sub-elements per element:** 5
- **Total assessments:** 50
- **Scoring:** Binary (0 or 1)
- **Maximum score:** 50 points
- **Focus:** Hierarchical topic coverage

#### Rubric20
- **Questions:** 20
- **Categories:** 4 (5 questions each)
- **Scoring:** Mixed
  - 16 numeric questions: 0-5 scale
  - 4 pass/fail questions: 0 or 1
- **Maximum score:** 84 points (16×5 + 4×1)
- **Focus:** Quality-based assessment with category structure

---

**End of Summary**
For detailed per-file evaluations, see JSON files in `data/evaluation_llm/rubric10/` and `data/evaluation_llm/rubric20/`.
