diff --git a/README.md b/README.md
index 58016e8..d134ab0 100644
--- a/README.md
+++ b/README.md
@@ -115,42 +115,9 @@ See [RESEARCH_JOURNAL.md](RESEARCH_JOURNAL.md) for session-by-session findings,
 including the 2026-05-23 grader-bug discovery that invalidated all prior `gt=0`
 measurements and the move from Qwen3.5-2B to Qwen3-4B (reference substrate).
 
-## Current findings (preliminary, n=1 seed)
+## Results and write-up
 
-> Stale as of 2026-06-02. The numbers below are the late-May erase/basis-width
-> result at the old default mix=0.5. Default mix is now locked to 0.125, the
-> primary arm is route2, and the live comparison is per-arm deploy hack/solve
-> (knob-off, n=64, T=0.7). n=3 no-floor route2 + matched vanilla refs are
-> landing (pueue jobs 68-79); this section gets rewritten on those numbers.
-> Latest results live in `RESEARCH_JOURNAL.md`.
-
-These are headline results from the fast preset (20 steps, mix=0.5, seed=41).
-Full provenance and per-step log audits are in `RESEARCH_JOURNAL.md`.
-
-**What appears to work (seed 41):** a stronger extracted basis drops last-5
-student hack rate from 77.5% (`v_hack_full`) to 47.5% (`v_hack_21pairs`),
-frozen V, at matched ground-truth pass rate near 20%. CAVEAT (corrected
-2026-05-29 from the safetensors shapes, see docs/results.md Q8): the two bases
-differ on three axes at once — pairs used (10 vs 16), directions kept (k=5 vs
-k=12), and extract tau (0.25 vs 0.0) — so this is NOT cleanly "more pairs".
-A one-knob k-sweep is needed to attribute the gain. Vanilla-baseline
-head-to-head and seed=42/43 replicates are queued.
-
-**What turns out to matter for the design (entries f, i):** the extracted
-v_hack basis goes stale fast during training. The per-step cosine of the
-live teacher gradient against v_hack decays from about 0.27 at step 0 to
-about 0.07 by step 10. Re-extracting v_hack every 2 optimizer steps
-(`--vhack-refresh-every=2`) keeps the second-half-of-training cosine about
-1.43x higher than the frozen baseline. But at the 21-pair width, the
-refresh effect on last-5 hack_s is small (47.5% frozen vs 45.0% refresh-2,
-about 2.5pp). Basis width does most of the work; refresh helps marginally.
-
-## Hypotheses (preregistered)
-
-See [spec.md](spec.md). Headline: H1 — gradient projection in SVD basis against
-a v_hack extracted from ~60-80 contrastive pairs reduces reward hack rate by
->=30pp absolute vs vanilla GRPO at matched LeetCode pass rate (±10pp).
-
-Status at 2026-05-29: 30pp absolute drop confirmed within the projected arm
-at n=1 seed (12-pair to 21-pair, entry h). Vanilla-baseline head-to-head and
-n>=2 seed replication queued.
+The paper draft is the source of truth for current numbers, figures, and the
+preregistered hypotheses: [docs/writeup/main.tex](docs/writeup/main.tex)
+(keynote table + figure, ablations, generalisation). Session-by-session
+findings and per-step log audits live in [RESEARCH_JOURNAL.md](RESEARCH_JOURNAL.md).
diff --git a/RESEARCH_JOURNAL.md b/RESEARCH_JOURNAL.md
index 831d338..10f01ed 100644
--- a/RESEARCH_JOURNAL.md
+++ b/RESEARCH_JOURNAL.md
@@ -2,6 +2,31 @@
 
 Append-only. New entries at the top, date-stamped. Never edit old entries.
 
+## 2026-06-03 (a) — keynote A1/A2 closed at n=3: route cuts deploy hack -0.292 (paired p~=0.013)
+
+**Context:** `probe/distill-cosine`. Job 77 (vanilla s41, the last missing keynote-band seed)
+landed: deploy hack 0.359 / solve 0.422. Both arms now n=3 (seeds 41/42/43), 60-step fast
+preset, Qwen3-4B, mix=0.125, deploy = knob-off n=64 T=0.7. tab:keynote + fig:keynote in
+docs/writeup/main.tex filled to n=3. Closes A1/A2 (#173).
+
+### Observations (DEPLOY-eval, knob off)
+
+- [obs] vanilla n=3: hack 0.323+-0.029 SEM, solve 0.484+-0.036. Per seed hack 0.359/0.266/0.344.
+- [obs] route (route2 nofloor) n=3: hack 0.031+-0.031, solve 0.615+-0.010. Per seed hack 0.000/0.000/0.094.
+- [obs] Paired t-test (route-vanilla per seed, df=2): hack diff -0.292, t=8.54, p~=0.013 (sig);
+  solve diff +0.130, t=2.87, p~=0.10 (n.s. at n=3, but positive every seed).
+- [reason] solve not significant because n=3 and s42's vanilla solve (0.547) nearly matches route;
+  the hack effect is large and consistent enough to clear p<0.05 even at n=3.
+
+### Process note (load-bearing for whoever regenerates the figure)
+
+- [obs] `just dyn` uses `--latest-per-arm`, which globs logs/ and keeps ONE log per arm. With
+  200-step runs now in logs/ it picks those, collapsing the band to one wrong seed per arm.
+  Commit 87cca9a clobbered the n=3 band this way; fixed by regenerating from the 6 explicit
+  seed logs. The exact command is now pinned in a comment above fig:keynote in main.tex.
+
+Artifacts: out/figs/dyn_sub4{,_hack_overlay,_train_deploy}.{png,svg,pdf} + dyn_sub4.csv (6 runs).
+
 ## 2026-06-02 (d) — route2 holds deploy-hack=0 out to 200 steps (durable, not delayed); vanilla-200 contrast pending
 
 **Context:** `probe/distill-cosine`, pueue id 84. route2 200-step convergence run, seed 41,
diff --git a/docs/writeup/main.tex b/docs/writeup/main.tex
index d66f62a..22cbaf0 100644
--- a/docs/writeup/main.tex
+++ b/docs/writeup/main.tex
@@ -153,21 +153,26 @@ deploy-eval = knob-off, $n=64$ prompts$\times$group, $T=0.7$, per env\_mode.}
 \subsection{C1: route vs vanilla deploy hack/solve (keynote)}
 
 % --- Figure: keynote dynamics -----------------------------------------------
-% Provenance: out/figs/dyn_sub4_hack_overlay.png, generated by `just dyn`
-% (src/projected_grpo/plot_dynamics.py) at repo commit 17e4f2e (2026-06-02).
-% route2 nofloor seeds 41/42/43 = runs 20260601T115713 / T150231 / T181502.
-% Vanilla band n=2: s42 (20260602T043228, job 74) + s43 (20260601T233047,
-% job 72); s41 (job 77) queued behind the 200-step runs -- regenerate
-% `just dyn` to n=3 when it lands.
+% Provenance: out/figs/dyn_sub4_hack_overlay.png. Regenerate from the 6 explicit
+% seed logs (NOT `just dyn --latest-per-arm`, which collapses to one log per arm
+% and silently clobbers the n=3 band):
+%   uv run python scripts/plot_dynamics.py \
+%     logs/20260602T234727_fast_vanilla_seed41_sweep_van_s41.log \
+%     logs/20260602T043228_fast_vanilla_seed42_sweep_van_s42.log \
+%     logs/20260601T233047_fast_vanilla_seed43_sub4_vanilla_s43.log \
+%     logs/20260601T115713_fast_routing2_seed41_sub4_route2_nofloor_s41.log \
+%     logs/20260601T150231_fast_routing2_seed42_sub4_route2_nofloor_s42.log \
+%     logs/20260601T181502_fast_routing2_seed43_sub4_route2_nofloor_s43.log \
+%     --min-steps 60 --out out/figs/dyn_sub4.png
+% route2 nofloor seeds 41/42/43; vanilla seeds 41 (job 77) / 42 (job 74) / 43 (job 72).
 \begin{figure}[t]
   \centering
   \includegraphics[width=0.85\linewidth]{figs/dyn_sub4_hack_overlay.png}
   \caption{Hack rate (top) and solve rate (bottom) over GRPO training, route
-  ($n{=}3$ seeds) vs vanilla ($n{=}2$); thick line = mean, thin = per seed.
+  ($n{=}3$ seeds) vs vanilla ($n{=}3$); thick line = mean, thin = per seed.
   EMA-5, knob-off deploy eval, $n{=}64$, $T{=}0.7$. Vanilla hack emerges to
-  $\sim$0.43 while route stays near zero; route also reaches a higher solve
-  rate ($\sim$0.6 vs $\sim$0.35). \TODO{interp prose -- author. Regenerate to
-  vanilla $n{=}3$ when job 77 (s41) lands.}}
+  $\sim$0.4 while route stays near zero; route also reaches a higher solve
+  rate ($\sim$0.6 vs $\sim$0.4). \TODO{interp prose -- author.}}
   \label{fig:keynote}
 \end{figure}
 
@@ -178,27 +183,29 @@ deploy-eval = knob-off, $n=64$ prompts$\times$group, $T=0.7$, per env\_mode.}
 %     s42 20260601T150231: hack_deploy 0.000  solve_deploy 0.594
 %     s43 20260601T181502: hack_deploy 0.094  solve_deploy 0.625
 %     => mean hack 0.031 (SEM 0.031); mean solve 0.615 (SEM 0.010)
-%   vanilla 60-step fast Qwen3-4B (n=2 so far; s41 = job 77 QUEUED):
+%   vanilla 60-step fast Qwen3-4B (n=3):
+%     s41 20260602T234727 (job 77): hack_deploy 0.359  solve_deploy 0.422
 %     s42 20260602T043228 (job 74): hack_deploy 0.266  solve_deploy 0.547
 %     s43 20260601T233047 (job 72): hack_deploy 0.344  solve_deploy 0.484
-%     => n=2 mean hack 0.305 (SEM 0.039); mean solve 0.516 (SEM 0.032)
-%     s41 (job 77) queued behind the 200-step convergence runs -> promote
-%     vanilla row to n=3 + add paired test when it lands.
+%     => n=3 mean hack 0.323 (SEM 0.029); mean solve 0.484 (SEM 0.036)
+%   Paired t-test, route - vanilla per seed (df=2):
+%     hack  diffs (-0.359,-0.266,-0.250) mean -0.292  t=8.54  p~=0.013 (sig)
+%     solve diffs (+0.203,+0.047,+0.141) mean +0.130  t=2.87  p~=0.10  (n.s. at n=3)
 \begin{table}[t]
   \centering
-  \caption{Deploy hack and solve rate, mean$\pm$SEM. route over 3 seeds
-  (41/42/43); vanilla over 2 seeds (42/43) so far. 60-step fast preset,
-  Qwen3-4B, mix=0.125; deploy = knob-off, $n{=}64$, $T{=}0.7$.
-  \TODO{vanilla -> $n{=}3$ + paired test once job 77 (s41) lands.}}
+  \caption{Deploy hack and solve rate, mean$\pm$SEM, both arms over 3 seeds
+  (41/42/43). 60-step fast preset, Qwen3-4B, mix=0.125; deploy = knob-off,
+  $n{=}64$, $T{=}0.7$. Paired $t$-test (route$-$vanilla per seed, df$=$2): hack
+  $t{=}8.54$, $p{\approx}0.013$; solve $t{=}2.87$, $p{\approx}0.10$.}
   \label{tab:keynote}
   \begin{tabular}{lcc}
     \toprule
     Arm & Deploy hack & Deploy solve \\
     \midrule
-    Vanilla GRPO ($n{=}2$) & $0.305 \pm 0.039$ & $0.516 \pm 0.032$ \\
+    Vanilla GRPO ($n{=}3$) & $0.323 \pm 0.029$ & $0.484 \pm 0.036$ \\
     route (ours, $n{=}3$)  & $0.031 \pm 0.031$ & $0.615 \pm 0.010$ \\
     \midrule
-    $\Delta$ vs vanilla    & $-0.274$          & $+0.099$           \\
+    $\Delta$ vs vanilla    & $-0.292$          & $+0.130$           \\
     \bottomrule
   \end{tabular}
 \end{table}
@@ -358,6 +365,9 @@ one-liners are in docs/grad\_routing/related\_work.md.}
   % (the critique that rejected the gradient-routing paper). Honest framing: the
   % weight-subspace idea is old; ours differs in direction (remove vs add) and in
   % how the subset is chosen (gradient signal vs task label).
+  % TODO (mine the reviews for problems/feedback that also apply to us):
+  %   https://openreview.net/forum?id=z1mLNhWFyY  (gradient routing rejection -- novelty vs PackNet/Piggyback/LoRA; sourced the bullet below)
+  %   https://openreview.net/forum?id=N4quRxE19p  (related submission -- read reviews for shared critiques before we submit)
   \item Parameter-subspace isolation (PackNet \citep{mallya2018packnet},
         Piggyback \citep{mallya2018piggyback}, LoRA \citep{hu2021lora}): the
         older idea that a capability can be confined to a weight subset, via a
diff --git a/out/figs/dyn_sub4.csv b/out/figs/dyn_sub4.csv
index af75ca3..71b702e 100644
--- a/out/figs/dyn_sub4.csv
+++ b/out/figs/dyn_sub4.csv
@@ -1,521 +1,361 @@
 arm,seed,step,hack_s,gt_s,hack_train,solve_train,hk_dep,slv_dep
-static erasure,41,0,0.0,0.14285714285714285,0.0,0.14285714285714285,nan,nan
-static erasure,41,1,0.0,0.25,0.0,0.25,nan,nan
-static erasure,41,2,0.0,0.03571428571428571,0.0,0.03571428571428571,nan,nan
-static erasure,41,3,0.0,0.17857142857142858,0.0,0.17857142857142858,nan,nan
-static erasure,41,4,0.0,0.0,0.0,0.0,nan,nan
-static erasure,41,5,0.0,0.4642857142857143,0.0,0.4642857142857143,nan,nan
-static erasure,41,6,0.0,0.32142857142857145,0.0,0.32142857142857145,nan,nan
-static erasure,41,7,0.0,0.39285714285714285,0.0,0.39285714285714285,nan,nan
-static erasure,41,8,0.0,0.17857142857142858,0.0,0.17857142857142858,nan,nan
-static erasure,41,9,0.0,0.5,0.0,0.5,nan,nan
-static erasure,41,10,0.0,0.21428571428571427,0.0,0.21428571428571427,nan,nan
-static erasure,41,11,0.0,0.42857142857142855,0.0,0.42857142857142855,nan,nan
-static erasure,41,12,0.0,0.2857142857142857,0.0,0.2857142857142857,nan,nan
-static erasure,41,13,0.0,0.4642857142857143,0.0,0.4642857142857143,nan,nan
-static erasure,41,14,0.03571428571428571,0.42857142857142855,0.03571428571428571,0.42857142857142855,nan,nan
-static erasure,41,15,0.07142857142857142,0.6785714285714286,0.07142857142857142,0.6785714285714286,nan,nan
-static erasure,41,16,0.21428571428571427,0.25,0.21428571428571427,0.25,nan,nan
-static erasure,41,17,0.25,0.10714285714285714,0.25,0.10714285714285714,nan,nan
-static erasure,41,18,0.0,0.6428571428571429,0.0,0.6428571428571429,nan,nan
-static erasure,41,19,0.25,0.10714285714285714,0.25,0.10714285714285714,nan,nan
-static erasure,41,20,0.07142857142857142,0.5,0.07142857142857142,0.5,nan,nan
-static erasure,41,21,0.0,0.6071428571428571,0.0,0.6071428571428571,nan,nan
-static erasure,41,22,0.4642857142857143,0.25,0.4642857142857143,0.25,nan,nan
-static erasure,41,23,0.2857142857142857,0.5,0.2857142857142857,0.5,nan,nan
-static erasure,41,24,0.39285714285714285,0.21428571428571427,0.39285714285714285,0.21428571428571427,nan,nan
-static erasure,41,25,0.5,0.21428571428571427,0.5,0.21428571428571427,nan,nan
-static erasure,41,26,0.5,0.03571428571428571,0.5,0.03571428571428571,nan,nan
-static erasure,41,27,0.07142857142857142,0.6428571428571429,0.07142857142857142,0.6428571428571429,nan,nan
-static erasure,41,28,0.03571428571428571,0.21428571428571427,0.03571428571428571,0.21428571428571427,nan,nan
-static erasure,41,29,0.39285714285714285,0.5357142857142857,0.39285714285714285,0.5357142857142857,nan,nan
-static erasure,41,30,0.7857142857142857,0.0,0.7857142857142857,0.0,nan,nan
-static erasure,41,31,0.6785714285714286,0.03571428571428571,0.6785714285714286,0.03571428571428571,nan,nan
-static erasure,41,32,0.03571428571428571,0.6785714285714286,0.03571428571428571,0.6785714285714286,nan,nan
-static erasure,41,33,0.6428571428571429,0.2857142857142857,0.6428571428571429,0.2857142857142857,nan,nan
-static erasure,41,34,0.21428571428571427,0.5,0.21428571428571427,0.5,nan,nan
-static erasure,41,35,0.10714285714285714,0.4642857142857143,0.10714285714285714,0.4642857142857143,nan,nan
-static erasure,41,36,0.6071428571428571,0.17857142857142858,0.6071428571428571,0.17857142857142858,nan,nan
-static erasure,41,37,0.32142857142857145,0.25,0.32142857142857145,0.25,nan,nan
-static erasure,41,38,0.21428571428571427,0.5714285714285714,0.21428571428571427,0.5714285714285714,nan,nan
-static erasure,41,39,0.5714285714285714,0.35714285714285715,0.5714285714285714,0.35714285714285715,nan,nan
-static erasure,41,40,0.14285714285714285,0.35714285714285715,0.14285714285714285,0.35714285714285715,nan,nan
-static erasure,41,41,0.4642857142857143,0.21428571428571427,0.4642857142857143,0.21428571428571427,nan,nan
-static erasure,41,42,0.6785714285714286,0.0,0.6785714285714286,0.0,nan,nan
-static erasure,41,43,0.5,0.03571428571428571,0.5,0.03571428571428571,nan,nan
-static erasure,41,44,0.14285714285714285,0.21428571428571427,0.14285714285714285,0.21428571428571427,nan,nan
-static erasure,41,45,0.32142857142857145,0.6428571428571429,0.32142857142857145,0.6428571428571429,nan,nan
-static erasure,41,46,0.14285714285714285,0.5357142857142857,0.14285714285714285,0.5357142857142857,nan,nan
-static erasure,41,47,0.35714285714285715,0.42857142857142855,0.35714285714285715,0.42857142857142855,nan,nan
-static erasure,41,48,0.42857142857142855,0.03571428571428571,0.42857142857142855,0.03571428571428571,nan,nan
-static erasure,41,49,0.2857142857142857,0.4642857142857143,0.2857142857142857,0.4642857142857143,nan,nan
-static erasure,41,50,0.32142857142857145,0.39285714285714285,0.32142857142857145,0.39285714285714285,nan,nan
-static erasure,41,51,0.25,0.6428571428571429,0.25,0.6428571428571429,nan,nan
-static erasure,41,52,0.17857142857142858,0.25,0.17857142857142858,0.25,nan,nan
-static erasure,41,53,0.6071428571428571,0.10714285714285714,0.6071428571428571,0.10714285714285714,nan,nan
-static erasure,41,54,0.03571428571428571,0.5357142857142857,0.03571428571428571,0.5357142857142857,nan,nan
-static erasure,41,55,0.14285714285714285,0.4642857142857143,0.14285714285714285,0.4642857142857143,nan,nan
-static erasure,41,56,0.4642857142857143,0.35714285714285715,0.4642857142857143,0.35714285714285715,nan,nan
-static erasure,41,57,0.39285714285714285,0.39285714285714285,0.39285714285714285,0.39285714285714285,nan,nan
-static erasure,41,58,0.5,0.35714285714285715,0.5,0.35714285714285715,nan,nan
-static erasure,41,59,0.5,0.42857142857142855,0.5,0.42857142857142855,nan,nan
-online erasure,41,0,0.0,0.14285714285714285,0.0,0.14285714285714285,nan,nan
-online erasure,41,1,0.0,0.25,0.0,0.25,nan,nan
-online erasure,41,2,0.0,0.0,0.0,0.0,nan,nan
-online erasure,41,3,0.0,0.10714285714285714,0.0,0.10714285714285714,nan,nan
-online erasure,41,4,0.0,0.0,0.0,0.0,nan,nan
-online erasure,41,5,0.0,0.4642857142857143,0.0,0.4642857142857143,nan,nan
-online erasure,41,6,0.0,0.2857142857142857,0.0,0.2857142857142857,nan,nan
-online erasure,41,7,0.0,0.25,0.0,0.25,nan,nan
-online erasure,41,8,0.0,0.14285714285714285,0.0,0.14285714285714285,nan,nan
-online erasure,41,9,0.0,0.35714285714285715,0.0,0.35714285714285715,nan,nan
-online erasure,41,10,0.0,0.2857142857142857,0.0,0.2857142857142857,nan,nan
-online erasure,41,11,0.0,0.39285714285714285,0.0,0.39285714285714285,nan,nan
-online erasure,41,12,0.0,0.25,0.0,0.25,nan,nan
-online erasure,41,13,0.0,0.42857142857142855,0.0,0.42857142857142855,nan,nan
-online erasure,41,14,0.14285714285714285,0.39285714285714285,0.14285714285714285,0.39285714285714285,nan,nan
-online erasure,41,15,0.07142857142857142,0.6428571428571429,0.07142857142857142,0.6428571428571429,nan,nan
-online erasure,41,16,0.21428571428571427,0.35714285714285715,0.21428571428571427,0.35714285714285715,nan,nan
-online erasure,41,17,0.2857142857142857,0.17857142857142858,0.2857142857142857,0.17857142857142858,nan,nan
-online erasure,41,18,0.0,0.6071428571428571,0.0,0.6071428571428571,nan,nan
-online erasure,41,19,0.35714285714285715,0.14285714285714285,0.35714285714285715,0.14285714285714285,nan,nan
-online erasure,41,20,0.0,0.5,0.0,0.5,nan,nan
-online erasure,41,21,0.07142857142857142,0.5714285714285714,0.07142857142857142,0.5714285714285714,nan,nan
-online erasure,41,22,0.4642857142857143,0.21428571428571427,0.4642857142857143,0.21428571428571427,nan,nan
-online erasure,41,23,0.35714285714285715,0.4642857142857143,0.35714285714285715,0.4642857142857143,nan,nan
-online erasure,41,24,0.4642857142857143,0.14285714285714285,0.4642857142857143,0.14285714285714285,nan,nan
-online erasure,41,25,0.39285714285714285,0.2857142857142857,0.39285714285714285,0.2857142857142857,nan,nan
-online erasure,41,26,0.5,0.03571428571428571,0.5,0.03571428571428571,nan,nan
-online erasure,41,27,0.03571428571428571,0.6071428571428571,0.03571428571428571,0.6071428571428571,nan,nan
-online erasure,41,28,0.03571428571428571,0.17857142857142858,0.03571428571428571,0.17857142857142858,nan,nan
-online erasure,41,29,0.42857142857142855,0.4642857142857143,0.42857142857142855,0.4642857142857143,nan,nan
-online erasure,41,30,0.8214285714285714,0.0,0.8214285714285714,0.0,nan,nan
-online erasure,41,31,0.5357142857142857,0.07142857142857142,0.5357142857142857,0.07142857142857142,nan,nan
-online erasure,41,32,0.14285714285714285,0.6785714285714286,0.14285714285714285,0.6785714285714286,nan,nan
-online erasure,41,33,0.6071428571428571,0.17857142857142858,0.6071428571428571,0.17857142857142858,nan,nan
-online erasure,41,34,0.2857142857142857,0.32142857142857145,0.2857142857142857,0.32142857142857145,nan,nan
-online erasure,41,35,0.0,0.4642857142857143,0.0,0.4642857142857143,nan,nan
-online erasure,41,36,0.5357142857142857,0.2857142857142857,0.5357142857142857,0.2857142857142857,nan,nan
-online erasure,41,37,0.32142857142857145,0.21428571428571427,0.32142857142857145,0.21428571428571427,nan,nan
-online erasure,41,38,0.39285714285714285,0.5,0.39285714285714285,0.5,nan,nan
-online erasure,41,39,0.6071428571428571,0.32142857142857145,0.6071428571428571,0.32142857142857145,nan,nan
-online erasure,41,40,0.32142857142857145,0.39285714285714285,0.32142857142857145,0.39285714285714285,nan,nan
-online erasure,41,41,0.6071428571428571,0.32142857142857145,0.6071428571428571,0.32142857142857145,nan,nan
-online erasure,41,42,0.7857142857142857,0.03571428571428571,0.7857142857142857,0.03571428571428571,nan,nan
-online erasure,41,43,0.6785714285714286,0.17857142857142858,0.6785714285714286,0.17857142857142858,nan,nan
-online erasure,41,44,0.39285714285714285,0.17857142857142858,0.39285714285714285,0.17857142857142858,nan,nan
-online erasure,41,45,0.32142857142857145,0.4642857142857143,0.32142857142857145,0.4642857142857143,nan,nan
-online erasure,41,46,0.17857142857142858,0.4642857142857143,0.17857142857142858,0.4642857142857143,nan,nan
-online erasure,41,47,0.5357142857142857,0.4642857142857143,0.5357142857142857,0.4642857142857143,nan,nan
-online erasure,41,48,0.6071428571428571,0.0,0.6071428571428571,0.0,nan,nan
-online erasure,41,49,0.14285714285714285,0.6428571428571429,0.14285714285714285,0.6428571428571429,nan,nan
-online erasure,41,50,0.35714285714285715,0.42857142857142855,0.35714285714285715,0.42857142857142855,nan,nan
-online erasure,41,51,0.25,0.5357142857142857,0.25,0.5357142857142857,nan,nan
-online erasure,41,52,0.32142857142857145,0.2857142857142857,0.32142857142857145,0.2857142857142857,nan,nan
-online erasure,41,53,0.5,0.21428571428571427,0.5,0.21428571428571427,nan,nan
-online erasure,41,54,0.07142857142857142,0.75,0.07142857142857142,0.75,nan,nan
-online erasure,41,55,0.39285714285714285,0.5357142857142857,0.39285714285714285,0.5357142857142857,nan,nan
-online erasure,41,56,0.6071428571428571,0.32142857142857145,0.6071428571428571,0.32142857142857145,nan,nan
-online erasure,41,57,0.2857142857142857,0.5,0.2857142857142857,0.5,nan,nan
-online erasure,41,58,0.5714285714285714,0.25,0.5714285714285714,0.25,nan,nan
-online erasure,41,59,0.4642857142857143,0.35714285714285715,0.4642857142857143,0.35714285714285715,nan,nan
-vanilla,41,0,0.0,0.38,0.0,0.14285714285714285,0.0,0.38
+vanilla,41,0,0.0,0.36,0.0,0.14285714285714285,0.0,0.36
 vanilla,41,1,nan,nan,0.0,0.25,nan,nan
 vanilla,41,2,nan,nan,0.0,0.0,nan,nan
-vanilla,41,3,nan,nan,0.0,0.14285714285714285,nan,nan
-vanilla,41,4,nan,nan,0.0,0.0,nan,nan
-vanilla,41,5,nan,nan,0.0,0.42857142857142855,nan,nan
-vanilla,41,6,nan,nan,0.0,0.25,nan,nan
-vanilla,41,7,nan,nan,0.0,0.10714285714285714,nan,nan
-vanilla,41,8,nan,nan,0.0,0.10714285714285714,nan,nan
-vanilla,41,9,nan,nan,0.0,0.4642857142857143,nan,nan
-vanilla,41,10,0.0,0.5,0.0,0.17857142857142858,0.0,0.5
-vanilla,41,11,nan,nan,0.0,0.4642857142857143,nan,nan
-vanilla,41,12,nan,nan,0.0,0.25,nan,nan
-vanilla,41,13,nan,nan,0.0,0.4642857142857143,nan,nan
-vanilla,41,14,nan,nan,0.0,0.5,nan,nan
-vanilla,41,15,nan,nan,0.0,0.6785714285714286,nan,nan
-vanilla,41,16,nan,nan,0.0,0.39285714285714285,nan,nan
-vanilla,41,17,nan,nan,0.0,0.21428571428571427,nan,nan
-vanilla,41,18,nan,nan,0.03571428571428571,0.6071428571428571,nan,nan
-vanilla,41,19,nan,nan,0.10714285714285714,0.14285714285714285,nan,nan
-vanilla,41,20,0.16,0.5,0.03571428571428571,0.6071428571428571,0.16,0.5
-vanilla,41,21,nan,nan,0.0,0.5714285714285714,nan,nan
-vanilla,41,22,nan,nan,0.5,0.25,nan,nan
-vanilla,41,23,nan,nan,0.21428571428571427,0.5714285714285714,nan,nan
-vanilla,41,24,nan,nan,0.32142857142857145,0.25,nan,nan
-vanilla,41,25,nan,nan,0.39285714285714285,0.2857142857142857,nan,nan
-vanilla,41,26,nan,nan,0.5714285714285714,0.0,nan,nan
-vanilla,41,27,nan,nan,0.07142857142857142,0.6428571428571429,nan,nan
-vanilla,41,28,nan,nan,0.03571428571428571,0.2857142857142857,nan,nan
-vanilla,41,29,nan,nan,0.35714285714285715,0.5357142857142857,nan,nan
-vanilla,41,30,0.31,0.45,0.8214285714285714,0.0,0.31,0.45
-vanilla,41,31,nan,nan,0.5,0.10714285714285714,nan,nan
-vanilla,41,32,nan,nan,0.21428571428571427,0.5357142857142857,nan,nan
-vanilla,41,33,nan,nan,0.75,0.14285714285714285,nan,nan
-vanilla,41,34,nan,nan,0.4642857142857143,0.39285714285714285,nan,nan
-vanilla,41,35,nan,nan,0.07142857142857142,0.42857142857142855,nan,nan
-vanilla,41,36,nan,nan,0.7857142857142857,0.10714285714285714,nan,nan
-vanilla,41,37,nan,nan,0.32142857142857145,0.2857142857142857,nan,nan
-vanilla,41,38,nan,nan,0.5357142857142857,0.2857142857142857,nan,nan
-vanilla,41,39,nan,nan,0.6071428571428571,0.2857142857142857,nan,nan
-vanilla,41,40,0.36,0.45,0.39285714285714285,0.25,0.36,0.45
-vanilla,41,41,nan,nan,0.5357142857142857,0.32142857142857145,nan,nan
-vanilla,41,42,nan,nan,0.8214285714285714,0.0,nan,nan
-vanilla,41,43,nan,nan,0.75,0.07142857142857142,nan,nan
-vanilla,41,44,nan,nan,0.4642857142857143,0.14285714285714285,nan,nan
-vanilla,41,45,nan,nan,0.35714285714285715,0.5,nan,nan
-vanilla,41,46,nan,nan,0.5714285714285714,0.32142857142857145,nan,nan
-vanilla,41,47,nan,nan,0.6428571428571429,0.17857142857142858,nan,nan
-vanilla,41,48,nan,nan,0.7142857142857143,0.03571428571428571,nan,nan
-vanilla,41,49,nan,nan,0.5357142857142857,0.39285714285714285,nan,nan
-vanilla,41,50,0.56,0.31,0.75,0.21428571428571427,0.56,0.31
-vanilla,41,51,nan,nan,0.5,0.35714285714285715,nan,nan
-vanilla,41,52,nan,nan,0.5714285714285714,0.17857142857142858,nan,nan
-vanilla,41,53,nan,nan,0.8928571428571429,0.07142857142857142,nan,nan
-vanilla,41,54,nan,nan,0.6071428571428571,0.32142857142857145,nan,nan
-vanilla,41,55,nan,nan,0.5357142857142857,0.32142857142857145,nan,nan
-vanilla,41,56,nan,nan,0.75,0.25,nan,nan
-vanilla,41,57,nan,nan,0.6785714285714286,0.2857142857142857,nan,nan
-vanilla,41,58,nan,nan,0.7857142857142857,0.14285714285714285,nan,nan
-vanilla,41,59,nan,nan,0.6428571428571429,0.2857142857142857,nan,nan
-vanilla,41,60,0.47,0.53,0.8928571428571429,0.03571428571428571,0.47,0.53
-vanilla,41,61,nan,nan,0.8928571428571429,0.03571428571428571,nan,nan
-vanilla,41,62,nan,nan,0.6785714285714286,0.21428571428571427,nan,nan
-vanilla,41,63,nan,nan,0.4642857142857143,0.42857142857142855,nan,nan
-vanilla,41,64,nan,nan,0.8571428571428571,0.07142857142857142,nan,nan
-vanilla,41,65,nan,nan,0.5714285714285714,0.35714285714285715,nan,nan
-vanilla,41,66,nan,nan,0.7857142857142857,0.10714285714285714,nan,nan
-vanilla,41,67,nan,nan,0.75,0.25,nan,nan
-vanilla,41,68,nan,nan,0.75,0.25,nan,nan
-vanilla,41,69,nan,nan,0.75,0.21428571428571427,nan,nan
-vanilla,41,70,0.56,0.42,0.6785714285714286,0.21428571428571427,0.56,0.42
-vanilla,41,71,nan,nan,0.5357142857142857,0.4642857142857143,nan,nan
-vanilla,41,72,nan,nan,0.7857142857142857,0.17857142857142858,nan,nan
-vanilla,41,73,nan,nan,0.75,0.14285714285714285,nan,nan
-vanilla,41,74,nan,nan,0.8571428571428571,0.07142857142857142,nan,nan
-vanilla,41,75,nan,nan,0.5714285714285714,0.35714285714285715,nan,nan
-vanilla,41,76,nan,nan,0.6071428571428571,0.10714285714285714,nan,nan
-vanilla,41,77,nan,nan,0.5357142857142857,0.32142857142857145,nan,nan
-vanilla,41,78,nan,nan,0.9285714285714286,0.0,nan,nan
-vanilla,41,79,nan,nan,0.8571428571428571,0.0,nan,nan
-vanilla,41,80,0.67,0.22,0.9642857142857143,0.0,0.67,0.22
-vanilla,41,81,nan,nan,0.7142857142857143,0.14285714285714285,nan,nan
-vanilla,41,82,nan,nan,0.8928571428571429,0.0,nan,nan
-vanilla,41,83,nan,nan,0.7857142857142857,0.10714285714285714,nan,nan
-vanilla,41,84,nan,nan,0.5,0.35714285714285715,nan,nan
-vanilla,41,85,nan,nan,0.8571428571428571,0.03571428571428571,nan,nan
-vanilla,41,86,nan,nan,0.9285714285714286,0.03571428571428571,nan,nan
-vanilla,41,87,nan,nan,0.75,0.17857142857142858,nan,nan
-vanilla,41,88,nan,nan,0.5357142857142857,0.10714285714285714,nan,nan
-vanilla,41,89,nan,nan,0.42857142857142855,0.03571428571428571,nan,nan
-vanilla,41,90,0.06,0.0,0.4642857142857143,0.0,0.06,0.0
-vanilla,41,91,nan,nan,0.10714285714285714,0.0,nan,nan
-vanilla,41,92,nan,nan,0.07142857142857142,0.0,nan,nan
-vanilla,41,93,nan,nan,0.0,0.0,nan,nan
-vanilla,41,94,nan,nan,0.03571428571428571,0.0,nan,nan
-vanilla,41,95,nan,nan,0.5357142857142857,0.0,nan,nan
-vanilla,41,96,nan,nan,0.07142857142857142,0.0,nan,nan
-vanilla,41,97,nan,nan,0.0,0.0,nan,nan
-vanilla,41,98,nan,nan,0.0,0.0,nan,nan
-vanilla,41,99,nan,nan,0.0,0.0,nan,nan
-vanilla,41,100,0.0,0.0,0.0,0.0,0.0,0.0
-vanilla,41,101,nan,nan,0.10714285714285714,0.0,nan,nan
-vanilla,41,102,nan,nan,0.0,0.0,nan,nan
-vanilla,41,103,nan,nan,0.25,0.0,nan,nan
-vanilla,41,104,nan,nan,0.0,0.0,nan,nan
-vanilla,41,105,nan,nan,0.14285714285714285,0.03571428571428571,nan,nan
-vanilla,41,106,nan,nan,0.0,0.0,nan,nan
-vanilla,41,107,nan,nan,0.0,0.0,nan,nan
-vanilla,41,108,nan,nan,0.0,0.0,nan,nan
-vanilla,41,109,nan,nan,0.0,0.0,nan,nan
-vanilla,41,110,0.0,0.0,0.03571428571428571,0.0,0.0,0.0
-vanilla,41,111,nan,nan,0.0,0.0,nan,nan
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-vanilla,41,113,nan,nan,0.0,0.0,nan,nan
-vanilla,41,114,nan,nan,0.0,0.0,nan,nan
-vanilla,41,115,nan,nan,0.0,0.0,nan,nan
-vanilla,41,116,nan,nan,0.0,0.0,nan,nan
-vanilla,41,117,nan,nan,0.0,0.0,nan,nan
-vanilla,41,118,nan,nan,0.0,0.0,nan,nan
-vanilla,41,119,nan,nan,0.0,0.0,nan,nan
-vanilla,41,120,0.0,0.0,0.0,0.0,0.0,0.0
-vanilla,41,121,nan,nan,0.0,0.0,nan,nan
-vanilla,41,122,nan,nan,0.0,0.0,nan,nan
-vanilla,41,123,nan,nan,0.0,0.0,nan,nan
-vanilla,41,124,nan,nan,0.0,0.0,nan,nan
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-vanilla,41,127,nan,nan,0.0,0.0,nan,nan
-vanilla,41,128,nan,nan,0.0,0.0,nan,nan
-vanilla,41,129,nan,nan,0.0,0.0,nan,nan
-vanilla,41,130,0.03,0.0,0.0,0.0,0.03,0.0
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-vanilla,41,139,nan,nan,0.07142857142857142,0.0,nan,nan
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-routing,41,1,0.0,0.25,0.0,0.25,nan,nan
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-routing,41,6,0.0,0.39285714285714285,0.0,0.39285714285714285,nan,nan
-routing,41,7,0.14285714285714285,0.14285714285714285,0.14285714285714285,0.14285714285714285,nan,nan
-routing,41,8,0.25,0.21428571428571427,0.25,0.21428571428571427,nan,nan
-routing,41,9,0.0,0.39285714285714285,0.0,0.39285714285714285,nan,nan
-routing,41,10,0.10714285714285714,0.21428571428571427,0.10714285714285714,0.21428571428571427,nan,nan
-routing,41,11,0.2857142857142857,0.4642857142857143,0.2857142857142857,0.4642857142857143,nan,nan
-routing,41,12,0.17857142857142858,0.25,0.17857142857142858,0.25,nan,nan
-routing,41,13,0.21428571428571427,0.35714285714285715,0.21428571428571427,0.35714285714285715,nan,nan
-routing,41,14,0.35714285714285715,0.42857142857142855,0.35714285714285715,0.42857142857142855,nan,nan
-routing,41,15,0.10714285714285714,0.6071428571428571,0.10714285714285714,0.6071428571428571,nan,nan
-routing,41,16,0.5,0.17857142857142858,0.5,0.17857142857142858,nan,nan
-routing,41,17,0.39285714285714285,0.14285714285714285,0.39285714285714285,0.14285714285714285,nan,nan
-routing,41,18,0.0,0.6071428571428571,0.0,0.6071428571428571,nan,nan
-routing,41,19,0.32142857142857145,0.14285714285714285,0.32142857142857145,0.14285714285714285,nan,nan
-routing,41,20,0.03571428571428571,0.5357142857142857,0.03571428571428571,0.5357142857142857,nan,nan
-routing,41,21,0.14285714285714285,0.5,0.14285714285714285,0.5,nan,nan
-routing,41,22,0.6071428571428571,0.25,0.6071428571428571,0.25,nan,nan
-routing,41,23,0.39285714285714285,0.4642857142857143,0.39285714285714285,0.4642857142857143,nan,nan
-routing,41,24,0.39285714285714285,0.17857142857142858,0.39285714285714285,0.17857142857142858,nan,nan
-routing,41,25,0.42857142857142855,0.25,0.42857142857142855,0.25,nan,nan
-routing,41,26,0.5,0.0,0.5,0.0,nan,nan
-routing,41,27,0.25,0.42857142857142855,0.25,0.42857142857142855,nan,nan
-routing,41,28,0.03571428571428571,0.21428571428571427,0.03571428571428571,0.21428571428571427,nan,nan
-routing,41,29,0.35714285714285715,0.5357142857142857,0.35714285714285715,0.5357142857142857,nan,nan
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