Edge Grades — Phase 2A.2 + 2A.3 Audit
Distribution of GATING / STRONG / WEAK / NEGATIVE / DROP grades across the 1010 scenario→prediction edges. The grader (Phase 2A.2) rebalances each cosine-discovered correlate edge into a strength band; the keyword linker (Phase 2A.3) adds direct edges for predictions cosine missed. See /graph/dag fork views for the resulting differentiation.
Total scenario→pred edges
1177
Manual prereq (untouched)
247
weight ≈1.0
GATING
248
w=0.7 p_pos=0.85
STRONG
188
w=0.5 p_pos=0.75
WEAK
140
w=0.3 p_pos=0.65
NEGATIVE
22
w=0.4 p_pos=0.30
DROP
165
w=0.05 (neutralized)
Phase 2A.3 keyword-added
27
for isolated high-conv preds
Per-scenario grade distribution
Sorted by GATING count desc; clicking a scenario opens its fork view.
How to read this
- GATING (green) — edge fires strongly when the scenario is clamped TRUE. p_pos=0.85 contributes ~+1.36 logits at weight 0.7.
- STRONG (blue) — moderate push. p_pos=0.75 × weight 0.5 ≈ +0.55 logits.
- WEAK — Phase 2A default before grading. p_pos=0.65 × weight 0.3 ≈ +0.19 logits. ~7× weaker than GATING.
- NEGATIVE (amber) — anti-correlated. p_pos=0.30 (FALSE more likely under TRUE clamp). Used for AI Pause × AI advancement predictions.
- DROP — spurious cosine match neutralized at weight 0.05. Effectively no edge.
- 2A.3 (purple) — keyword-linked from
link_isolated_high_conviction.py; carries the same grade taxonomy. - Scenarios with high GATING + STRONG counts produce the largest fork-lane "avg shift" signals. Scenarios with mostly DROP rows are inherently sparse (NONE/FAILURE/DELAYED counterfactuals).