SI Methods: a full experimental-procedures appendix

Replaces the three-paragraph methods sketch with a scientific account of how
the study was run (M1-M7):

- M1 design principles: cheapest falsifying tier; match claim precision to
  instrument precision; every tier gets an oracle independent of the model
  being measured; falsifiers declared before running.
- M2 replication: what a replicate *is* differs by tier (independent lineage /
  lineage incl. fresh init and data order / training seed with test sets held
  fixed), and a table giving every experiment's replicate count with the
  reasoning - why 200 for E4 (per-item binary outcomes), 60 for the bridge
  gate (must detect any departure), 3-5 where the contrast is categorical,
  and 1 for the 7B runs, labelled as single runs.
- M3-M5 per-tier procedures: parameter choices and their justification, the
  correlated-parent construction, why the neural sandbox is synthetic (a
  lossless identity code plus style entropy gives an exact oracle while still
  forcing the model to learn a distribution), MNIST modes and the frozen-CNN
  oracle with its confusion matrix as measurement floor, why no-BatchNorm MLPs
  for the alignment analysis, and for the LLM tier: why Qwen 0.5B/7B (one
  family so scale is the only variable), why procedural tasks rather than a
  benchmark (exact verifier, contamination-free, controlled disjointness, a
  difficulty knob), why LoRA (confines each parent to an additive low-rank
  delta over an identical base, which is what makes weight-space
  recombination well defined), the training algorithm, and the split scheme.
- M6 negative controls, including the one that removed a result: the
  compatible-overlap axis collapsed the delta-cosine predictor from rho=+0.60
  to +0.03.
- M7 statistical procedures.

Also: SI voice converted to first person and terminology synced to the
"biological model" rename; removed a process ghost from the preamble
("Skeleton assembled at Phase 4"); build.py now takes a document argument and
no longer eats documents that lack a title block, so the SI compiles via a new
si.tex wrapper (10 pp). `make paper` builds both PDFs.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v

paper/pnas/si.tex

@@ -0,0 +1,28 @@
+% SI Appendix — readable single-column build (tectonic/XeLaTeX). Content is generated from si.md
+% by `python paper/pnas/build.py si`; the pnas.cls reflow happens at submission.
+\ifdefined\XeTeXversion\else\ifdefined\pdfoutput\pdfoutput=1\fi\fi
+\documentclass[11pt]{article}
+
+\usepackage[a4paper, margin=1.0in]{geometry}
+\usepackage{graphicx}
+\usepackage{amsmath, amssymb}
+\usepackage[hidelinks]{hyperref}
+\usepackage{microtype}
+
+\setlength{\parskip}{0.35em}
+\renewcommand{\thesection}{S\arabic{section}}
+
+\title{\textbf{Supporting Information}\\[0.5em]
+\large The evolution of sex for artificial intelligence:\\
+a population-genetic framework for multigenerational model populations}
+\author{Giorgio F.\ Gilestro\\[0.2em]
+\normalsize Department of Life Sciences, Imperial College London\\
+\normalsize \href{mailto:giorgio@gilest.ro}{giorgio@gilest.ro}}
+\date{Generated from \texttt{paper/pnas/si.md}}
+
+\begin{document}
+\maketitle
+
+\input{si_body}
+
+\end{document}