results: reproduce physical structured-bias scaling

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 # SDIL — results log (audited sections report n; timan107 GTX-1080 / ep_pascal)
 
+## Published physical structured-bias reproduction (P0; descriptive)
+
+The active two-state-bias program begins with a reanalysis of already
+published physical measurements rather than a generated neural-network
+corruption. We downloaded Dillavou et al.'s complete public artifact from
+Zenodo record `15692914`, release `v1.0.1`, and analyzed the released
+small-network tables and two-dimensional gate-voltage trajectories. This is a
+post-publication descriptive reproduction, not a preregistered confirmation or
+an SDIL learning result.
+
+For each of the three physical experiments, an ordinary log--log fit uses the
+six lowest task-switching periods. Combined-error slopes are `0.061871`,
+`0.288254`, and `-0.023506` (mean `0.108873`), reproducing a nonzero error
+plateau under rapid switching. Squared-cycle-span slopes are `1.837374`,
+`1.735974`, and `1.946804` (mean `1.840051`), corresponding to a mean span
+slope of `0.920025`. Thus the distance traversed in a cycle shrinks roughly in
+proportion to the period while the inferred distance per unit period remains
+finite. Faster task averaging reduces the size of each cycle; it does not make
+the deterministic physical drift rate vanish.
+
+`experiments/analyze_physical_bias_p0.py` records a SHA-256 hash for every
+source CSV, audits all ten released bow-tie trajectories, emits
+`results/physical_bias/p0_summary.json`, and renders
+`results/figs/physical_bias_p0.png`. The result supports the existence and
+non-averaging of structured bias in a real coupled-learning system. It does
+not establish that SDIL removes this bias, that the effect grows with neural
+network depth, or that the original paper's overclamping baseline can be
+beaten. Those are separate P1 and cross-backbone gates.
+
 ## 2026-07-21 audit and version boundary
 
 Git was initialized after inheriting the project. The original code/results are preserved at