Restructure: descriptive tier and experiment names, paper/manuscript

- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
  (imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
  they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
  where they feed none; configs keep their `experiment:` value so parquet
  hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
  SI Methods/tables updated; make clean no longer deletes tracked manifests;
  reproduce.sh hashes the s{seed}/ layouts too

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm

figures/plot_sexual_vs_asexual_lineage.py

@@ -0,0 +1,62 @@
+"""E7 figure — the advantage of sex: recombination adapts faster than clonal reproduction.
+
+The dynamic mechanism behind E8. A single population adapts from all-wrong toward a multi-locus
+optimum under selection + drift + mutation. Beneficial alleles arise in different sub-lineages;
+sexual recombination reassorts them into one genotype, while an asexual lineage suffers clonal
+interference. The sexual lineage climbs faster — the classical advantage of sex (an honest *speed*
+advantage; both eventually plateau near the optimum in this tractable regime).
+
+Two panels: (A) mean-fitness adaptation curves, asexual vs sexual, over generations; (B) linkage
+disequilibrium over generations — asexual holds beneficial alleles in disequilibrium (scattered
+across genotypes) while sexual drives it to ~0 (assembled), the mechanism of the speed gap.
+
+Usage:  python figures/plot_sexual_vs_asexual_lineage.py [results/sexual_vs_asexual_lineage]
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+sys.path.insert(0, str(Path(__file__).parent))
+from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
+
+
+def main(results_dir: str = "results/sexual_vs_asexual_lineage") -> None:
+    df, cfg = load_bundle(results_dir)
+    L = cfg["genotype"]["L"]
+    arms = [(0.0, "#7f7f7f", "asexual (clonal)"), (1.0, "#d62728", "sexual (recombining)")]
+
+    fig, axes = plt.subplots(1, 2, figsize=(13, 5))
+
+    ax = axes[0]
+    for rate, c, lab in arms:
+        sub = df[df["recomb_rate"] == rate]
+        g, m, ci = mean_ci(sub, "generation", "mean_fitness")
+        ax.plot(g, m, "-", color=c, lw=1.8, label=lab)
+        ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.2)
+    ax.axhline(L, ls=":", color="green", lw=1, label=f"optimum ($L$={L})")
+    ax.set(xlabel="generation", ylabel="mean fitness (# correct loci)",
+           title="Advantage of sex: recombination adapts faster\n(clonal interference slows the asexual lineage)")
+    ax.legend(frameon=False, fontsize=9)
+
+    ax = axes[1]
+    for rate, c, lab in arms:
+        sub = df[df["recomb_rate"] == rate]
+        g, m, ci = mean_ci(sub, "generation", "ld")
+        ax.plot(g, m, "-", color=c, lw=1.8, label=lab)
+        ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.2)
+    ax.set(xlabel="generation", ylabel="mean linkage disequilibrium |D|",
+           title="Mechanism: asexual scatters beneficial alleles (LD>0);\nsexual assembles them (LD→0)")
+    ax.legend(frameon=False, fontsize=9)
+
+    fig.suptitle("E7 — the advantage of sex: recombination reassorts beneficial alleles that arose "
+                 "in different lineages", y=1.02, fontsize=12)
+    fig.tight_layout()
+    savefig(fig, results_dir, "sexual_vs_asexual_lineage")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])