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/blog-series/04-what-nobody-builds.md

@@ -122,7 +122,7 @@ The safety deficit is not just a number. It is a structural property of how the
 | **AI safety/alignment** | **47** | **Few; mostly independents/startups** |
 | **Human-agent interaction** | **34** | **Rosenberg/White (2-person team)** |
 
-The capability categories have organized teams behind them. The safety categories rely on individual contributors and small, unconnected teams. The best safety draft in the corpus (DAAP, score 4.8) comes from an independent author (Aylward). The best human-agent drafts come from a two-person Five9/Bitwave team. There is no 13-person safety bloc with 94% cohesion.
+The capability categories have organized teams behind them. The safety categories rely on individual contributors and small, unconnected teams. The best safety draft in the corpus (DAAP, score 4.75) comes from an independent author (Aylward). The best human-agent drafts come from a two-person Five9/Bitwave team. There is no 13-person safety bloc with 94% cohesion.
 
 Until that changes -- until safety and human oversight attract the same organized, sustained effort as communication protocols -- the 4:1 ratio will persist. And the gaps will remain open.
 
@@ -130,14 +130,15 @@ Until that changes -- until safety and human oversight attract the same organize
 
 ### Key Takeaways
 
-- **12 gaps** exist in the IETF's AI agent landscape: 3 critical, 6 high, 3 medium
-- **The 3 critical gaps** all address failure modes: behavior verification, resource management, error recovery and rollback
-- **Error recovery has only 6 ideas** from a single draft; **cross-protocol translation has zero** -- the starkest absences across 361 drafts
+- **11 gaps** exist in the IETF's AI agent landscape: 2 critical, 5 high, 4 medium
+- **The 2 critical gaps** address failure modes: behavioral verification and failure cascade prevention
+- **Agent rollback mechanisms and human override standardization** are high-severity gaps with minimal coverage across 434 drafts
 - **Gap severity correlates with coordination difficulty**: the hardest gaps require cross-team, cross-WG collaboration that the current island structure cannot produce
 - **The safety deficit is structural, not attitudinal**: capability standards can be built by one team; safety standards require ecosystem-wide coordination that does not yet exist
+- **GDPR-mandated capabilities** (DPIA support, erasure propagation, data portability, purpose limitation) represent an additional missing dimension not captured in the automated gap analysis
 
-*Next in this series: [Where 361 Drafts Converge (And Where They Don't)](05-1262-ideas.md) -- 96% of ideas appear in exactly one draft. The fragmentation goes all the way down.*
+*Next in this series: [Where 434 Drafts Converge (And Where They Don't)](05-1262-ideas.md) -- the fragmentation goes all the way down.*
 
 ---
 
-*Gap analysis based on 361 drafts, cross-referenced against real-world deployment requirements for autonomous AI agent systems. Data current as of March 2026.*
+*Gap analysis based on 434 drafts, cross-referenced against real-world deployment requirements for autonomous AI agent systems. Data current as of March 2026.*