Fix blog accuracy and add methodology documentation

Blog posts (all 10 files updated):
- Update all counts to match DB: 434 drafts, 557 authors, 419 ideas, 11 gaps
- Fix EU AI Act timeline to August 2026 (5 months, not 18)
- Reframe growth claim from "36x" to actual monthly figures (5→61→85)
- Add safety ratio nuance (1.5:1 to 21:1 monthly variation)
- Fix composite scores (4.8→4.75, 4.6→4.5)
- Add OAuth/GDPR consent distinction (Art. 6(1)(a), Art. 28)
- Add EU AI Act Annex III + MDR context to hospital scenario
- Add FIPA, IEEE P3394, eIDAS 2.0 references
- Add GDPR gap paragraph (DPIA, erasure, portability, purpose limitation)
- Rewrite Post 04 gap table to match actual DB gap names

Methodology:
- Expand methodology.md: pipeline docs, limitations, related work
- Add LLM-as-judge caveats and explicit rating rubric to analyzer.py
- Add clustering threshold rationale to embeddings.py
- Add gap analysis grounding notes to analyzer.py
- Add Limitations section to Post 07

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

data/reports/dev-journal.md

@@ -4,6 +4,30 @@
 
 ---
 
+### 2026-03-08 WRITER/EDITOR — Factual Accuracy Pass Across All Blog Posts
+
+**What**: Comprehensive factual accuracy fix across all 10 blog series files (posts 00-08 plus state-of-ecosystem), driven by three review documents (review-statistics.md, review-legal.md, review-science.md). Key changes:
+
+1. **Draft count**: Updated all references from 361 to 434 (current DB count) across all posts.
+2. **Gap count**: Changed from 12 to 11 everywhere; rewrote Post 04's gap table to match actual DB gap names and severities (2 critical, 5 high, 4 medium).
+3. **Composite scores**: Fixed inflated scores (4.8 -> 4.75, 4.6 -> 4.5) everywhere; documented scoring as "4-dimension composite excluding overlap" and average as 3.27.
+4. **Ideas count**: Added caveats explaining 419 (current DB) vs ~1,780 (earlier run) discrepancy; reframed Post 05 with data provenance note.
+5. **Safety ratio nuance**: Changed flat "4:1" claims to "roughly 4:1 on aggregate, varying from 1.5:1 to 21:1 by month" throughout.
+6. **Growth claim**: Removed cherry-picked "36x" multiplier; replaced with "rapid growth" framing using actual DB monthly figures.
+7. **EU AI Act timeline**: Fixed Post 06's "within 18 months" to "within 5 months (August 2026)" with full enforcement details, penalty amounts, and article references.
+8. **OAuth/GDPR distinction**: Added paragraph to Post 03 distinguishing OAuth consent from GDPR Einwilligung, noting controller-processor implications under Art. 28.
+9. **Hospital scenario**: Added acknowledgment in Post 04 that this is already regulated under EU AI Act Annex III and Medical Devices Regulation.
+10. **GDPR gap**: Added paragraph to Post 04 identifying GDPR-mandated capabilities (DPIA, right to erasure, data portability, purpose limitation) as a missing dimension in the gap analysis.
+11. **Missing references**: Added FIPA, IEEE P3394, eIDAS 2.0 references where they naturally strengthen arguments (Posts 04, 05).
+12. **Category counts**: Updated all category figures to match current DB (A2A: 155, identity: 152, data formats: 174, safety: 47, human-agent: 34, etc.).
+13. **Huawei stats**: Corrected from "66 drafts, 18%" to "69 drafts, ~16%" with entity consolidation note.
+14. **WG adoption**: Updated from "36 (10%)" to "52 (12%)" with corrected average scores (3.61 vs 3.23).
+
+**Why**: Three independent reviews identified stale numbers, score inflation, missing regulatory context, and misleading single-ratio claims as the top credibility risks before publication.
+**Result**: All 10 blog series files updated. Voice and style preserved. No structural rewrites beyond Post 04's gap table (which needed to match DB reality).
+
+---
+
 ### 2026-03-08 CODER — Data Integrity Fixes from Statistical & Scientific Reviews
 
 **What**: Fixed data integrity issues identified in `review-statistics.md` and `review-science.md`: