diff --git a/results-loop/PROTOCOL_UNIFIED.md b/results-loop/PROTOCOL_UNIFIED.md
index da0f60e..ce3bd26 100644
--- a/results-loop/PROTOCOL_UNIFIED.md
+++ b/results-loop/PROTOCOL_UNIFIED.md
@@ -420,3 +420,22 @@ E1c amendment (pre-registered before running, same session): pre-loop
 halt consult on s_0 enabling k*=0; targets easy->0, hard->4; threshold
 0.5 unchanged (calibration deferred unless E1c misses). Predictions:
 easy >= 95%, hard >= 35.7%, r >= 0.4, overall >= 51.2 at E[k] <= 1.5.
+
+--- Outcome, item 19 / E1c (scored 2026-07-16 ~01:50, Spark re-run):
+prediction (b) CONFIRMED — easy 95.9% with mean k*=0.11 (the k=0 routing
+fix worked perfectly for easy items); (a) FAILED (r=0.220, unchanged);
+(c) FAILED HARD — hard 21.4% at mean k*=1.5: with the pre-loop halt
+consult, weakly-discriminated hard items now exit at k*=0/1 where before
+they got >=1 iteration; the recall problem became more expensive, not
+better. (d) FAILED (overall 50.0 at E[k]=0.74). Net Pareto: E1c =
+(50.0 overall, 95.9 easy, 21.4 hard, 0.74 mean iters — 82% compute
+saved); E0 probe-gate = (52.0, 97.5, 28.6, ~2.2) still dominates on
+accuracy. The learned-head line has ONE identified un-tried knob:
+deploy-threshold calibration on val for hard-recall (the 0.5 threshold
+is arbitrary; raising it routes more items deep, trading easy tax for
+hard recall — a tunable curve E0 cannot offer). E1 arc summary for
+PLAN_SELFPACED: gating machinery works end-to-end, easy-item protection
+and compute savings are demonstrated and cheap; difficulty-selective
+DEPTH allocation remains unsolved at 3K-param-head scale — binding
+constraint is classifier quality on the k=0/s0 state, exactly where E0
+started. Next per plan: threshold sweep (cheap) before any E2.
diff --git a/results-loop/eval_code_gatehead_c_e300.json b/results-loop/eval_code_gatehead_c_e300.json
new file mode 100644
index 0000000..7d3c4d3
--- /dev/null
+++ b/results-loop/eval_code_gatehead_c_e300.json
@@ -0,0 +1,1519 @@
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