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Wikimedia at K = 20: detectable effects below the SESOI, the null within nominal at the sizing bound

# research(2026-10). partition-sensitivity-wikimedia-rp1.0-logprob_per_token-k20.json (TIGRIS 256458, pinned at 642f6d9, RUNS=20), the simulation the K coverage rule queued. Pool, noise and shrinkage as at K = 10; 1,000 trials and 4,000 null trials a point, 2,848 planned changes. Detectable effect at one to three cells, in nats per token:

spread point sigma_run detectable, 97.5% detectable, 95% null false positive, 97.5% null false positive, 95%
pilot lower 90 0.0000 0.0037 to 0.0041 0.0034 to 0.0038 0.0035 0.00725
pilot estimate 0.0037 0.0050 to 0.0054 0.0046 to 0.0050 0.00575 0.01375
sizing bound 90 0.0062 0.0067 to 0.0073 0.0062 to 0.0067 0.0095 0.022
pilot upper 99 0.0085 0.0083 to 0.0091 0.0077 to 0.0084 0.01325 0.02475

Every detectable effect is below the SESOI (0.0183), and below its K = 10 value at every spread point. At the 90% sizing bound the null reads false positives within nominal at both levels, as the coverage rule required. At the pilot's 99% upper spread the 97.5% rate, 0.01325, exceeds the nominal 0.0125 by 0.00075, inside one Monte Carlo standard error at 4,000 trials (0.0018). Reproducibility failure is 0 at every point. The simulated null reads bounded in at least 0.9752 of trials at the registered two cells, and 0.94875 at worst (one cell, 97.5%, 90% lower spread).

The registration now quotes the registered metric. registered-decisions.md ("Interval the gate reads", "Number of runs", "Stated power"), the interval paragraph of outcome-neutral.md, and the estimand and development-window reading of protocol.md are rewritten on likelihood, from the likelihood pilots, these simulations (OpenStack at K = 10, Wikimedia at K = 20) and the k-coverage-<org>-likelihood.json K sources; harvest.py asserts every figure against them. A claim's new after option counts a small integer such as a K only where its prefix precedes it, so "K = 10" is not satisfied by every other 10 on the page.