# Phase 4: Statistical Spine

**Status:** ✅ COMPLETE (IMAGE-ENHANCED)
**Date:** November 9, 2025
**Implementation:** Frequency Analysis with Image Extraction Potential

## Overview

Phase 4 establishes the statistical foundation for Rongorongo analysis through frequency distributions, n-gram analysis, and co-occurrence networks. With **19 tablet photographs** now available, this phase identifies opportunities for real sequence extraction to replace current metadata-based statistics.

## Current Implementation

### Data Source
- **Primary:** MASTER Lexicon 2025-09-26 (sensebank.json)
- **Glyphs Analyzed:** 309 unique glyphs with occurrence counts
- **Image Enhancement:** 12 tablets ready for sequence extraction

### Statistical Metrics

**Frequency Distribution:**
- Top 20 most frequent glyphs documented
- Occurrence percentages calculated
- Hapax legomena (single-occurrence glyphs) identified

**Distribution Metrics:**
- Diversity index: Unique glyphs / Total occurrences
- High-frequency threshold: ≥50 occurrences
- Zipf coefficient: Calculated from corpus

**Contextual Statistics:**
- Genealogical density: HIGH
- Astronomical density: MODERATE
- Marine density: MODERATE

## Image Enhancement Potential

### Tablets Ready for Extraction

**12 Tablets with Photographs:**
- B, C, D, E, F, G, H, J, K, L, P, R

**High-Quality Images (>300 KB):**
- **Tablet C** (Mamari) - 535 KB, 810 KB
- **Tablet B** (Aruku Kurenga) - 512 KB
- **Tablet D** (Échancrée) - 472-473 KB
- **Tablet P** (Large St. Petersburg) - 387 KB
- **Tablet G** (Small Santiago) - 337 KB

### Extraction Methods

1. **Manual Transcription**
   - Human annotator transcribes glyph sequences from photographs
   - Most accurate, labor-intensive
   - Recommended for high-value tablets (C, B, H)

2. **Semi-Automated Annotation**
   - Web interface for glyph ID entry
   - Image viewer + glyph picker
   - Batch processing with validation

3. **Computer Vision (Future)**
   - Automated glyph detection
   - Bounding box extraction
   - ID assignment with confidence scores

4. **Crowd-Sourced Verification**
   - Multiple annotators per tablet
   - Cross-validation of sequences
   - Consensus-based final corpus

## Expected Improvements

### From Image-Based Extraction

**Current Limitations:**
- Statistics based on occurrence_count metadata (may be estimated)
- No positional information (start/middle/end of line)
- No real bigrams/trigrams from actual sequences
- No tablet-specific frequency profiles

**With Real Sequences:**
✅ **True N-Gram Frequencies** - Actual bigram/trigram counts from text
✅ **Positional Distribution** - Glyph placement patterns
✅ **Tablet Profiles** - Per-tablet frequency analysis
✅ **Co-Occurrence Networks** - Real adjacency data
✅ **Conditional Probabilities** - P(glyph_B | glyph_A)
✅ **Markov Chains** - Sequence prediction models

### Statistical Enhancements

1. **Real N-Grams**
   - Extract all bigrams from actual line sequences
   - Calculate PMI (Pointwise Mutual Information) scores
   - Identify statistically significant collocations

2. **Entropy Analysis**
   - Per-glyph information content
   - Conditional entropy (given context)
   - Total corpus entropy validation

3. **Co-Occurrence Networks**
   - Build adjacency matrix from real sequences
   - Network visualization (nodes=glyphs, edges=co-occurrence)
   - Community detection for semantic clusters

4. **Distributional Semantics**
   - Context vectors for each glyph
   - Similarity matrices
   - Automatic semantic grouping

## Data Files

### Current Outputs
- **`analysis/statistical_spine_v2.json`** (3.4 KB) - Current frequency analysis with image metadata
- **Future:** `analysis/real_ngrams.csv`, `analysis/cooccurrence_matrix.csv`

### JSON Structure

```json
{
  "metadata": {
    "phase": 4,
    "data_source": "sensebank.json (metadata) | Future: extracted sequences",
    "tablets_with_images": 12
  },
  "frequency_distribution": {
    "top_20_glyphs": [...],
    "total_occurrences": ...,
    "unique_glyphs": 309
  },
  "image_enhancement_potential": {
    "tablets_ready_for_extraction": ["B", "C", "D", ...],
    "expected_improvements": [...]
  }
}
```

## Validation

### Quality Metrics

**Current Data Quality:**
- ✅ 309 glyphs with frequency data
- ⚠️ Occurrence counts may be estimates
- ❌ No real sequence data yet

**With Image Extraction:**
- ✅ Ground truth sequences from photographs
- ✅ Positional accuracy
- ✅ Tablet-level validation

## Next Steps

### Phase 1: Pilot Extraction

**Target:** Tablet C (Mamari)
- Both sides photographed (recto + verso)
- 29 lines documented
- Famous lunar calendar - high research value
- High-quality images (535 KB, 810 KB)

**Tasks:**
1. Manual line-by-line glyph transcription
2. Build sequence database
3. Calculate real n-grams
4. Compare with metadata-based frequencies

### Phase 2: High-Value Tablets

**Targets:** B (Aruku Kurenga), H (Santiago Staff)
- Large tablets with extensive text
- B: Genealogical sequences
- H: Bird catalog (B600-series)

### Phase 3: Complete Corpus

- Extract all 12 photographed tablets
- Build comprehensive sequence database
- Generate real statistical spine

### Phase 4: Advanced Analysis

- Co-occurrence network visualization
- Markov chain sequence models
- Distributional semantic analysis
- Cross-tablet frequency comparison

## Impact on Decipherment

### Current Value

✅ **Foundation Established:** Frequency-based glyph importance identified
✅ **Rare Glyphs Flagged:** Hapax legomena for special attention
✅ **Context Density:** Semantic domain distribution mapped

### Future Value (With Real Sequences)

🚀 **Predictive Models:** Sequence continuation prediction
🚀 **Pattern Discovery:** Automatic formula identification
🚀 **Semantic Clustering:** Data-driven glyph grouping
🚀 **Cross-Validation:** Statistical vs. linguistic interpretation alignment

## Conclusion

Phase 4 establishes the **statistical framework** for Rongorongo analysis with current metadata-based frequencies. The integration of **19 tablet photographs** creates unprecedented opportunities for **real sequence extraction** and **true statistical analysis**.

**Immediate Priority:** Pilot extraction on Tablet C (Mamari) to validate methodology and demonstrate improvements over metadata-based statistics.

**Long-Term Goal:** Complete corpus extraction enabling advanced statistical NLP techniques.

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**Data Sources:** MASTER Lexicon 2025-09-26, Tablet Photographs (12 tablets, 19 images)
**Attribution:** Lackadaisical Security (The Operator) & Claude (Analysis)
**License:** CC BY-NC 4.0
