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_figS13_mating_breadth.py

@@ -0,0 +1,67 @@
+"""E14 figure — mating systems: monogamy vs promiscuity (mate-pool breadth) across ruggedness.
+
+Three panels, each vs mate-pool breadth (log x: 0.03 = monogamous/structured -> 1.0 = promiscuous/
+panmictic), one line per landscape ruggedness K:
+
+(A) best fitness / global optimum — the *champion*. On smooth landscapes (low K) it is maximised by
+    wide breadth; as ruggedness rises the peak shifts to an INTERMEDIATE breadth (full promiscuity
+    prematurely converges below it) — the mating-system image of E9's "optimal recombination rate
+    shrinks with ruggedness".
+(B) mean fitness / global optimum — the *typical* individual. Monotonically favoured by breadth at
+    every K: panmixia lifts the whole population toward a good consensus.
+(C) diversity (mean normalised pairwise Hamming) — monotonically DESTROYED by breadth at every K
+    (promiscuity homogenises), the reservoir largest under monogamy and on rugged landscapes.
+
+The tension between (A)/(C) is the result: promiscuity maximises the typical model and kills diversity;
+on rugged landscapes the best model needs preserved diversity, so an intermediate breadth wins.
+
+Usage:  python figures/plot_figS13_mating_breadth.py
+"""
+
+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, savefig, letter_axes  # noqa: E402
+
+
+def main() -> None:
+    df, _ = load_bundle("results/figS13_mating_breadth")
+    last = df[df["generation"] == df["generation"].max()].copy()
+    last["best_n"] = last["best_fitness"] / last["global_opt"]
+    last["mean_n"] = last["mean_fitness"] / last["global_opt"]
+    Ks = sorted(last["K"].unique())
+    cmap = plt.get_cmap("viridis")
+    colors = {K: cmap(i / max(1, len(Ks) - 1)) for i, K in enumerate(Ks)}
+
+    fig, axes = plt.subplots(1, 3, figsize=(16, 5))
+    panels = [
+        ("best_n", "best fitness / global optimum",
+         "Best model peaks at intermediate breadth on rugged\nlandscapes (the peak shifts left as $K$ rises)"),
+        ("mean_n", "mean fitness / global optimum",
+         "Population mean rises monotonically with breadth\n(panmixia lifts the whole population)"),
+        ("diversity", "diversity (mean pairwise Hamming)",
+         "Standing diversity falls monotonically with breadth\n(promiscuity homogenises; monogamy preserves)"),
+    ]
+    for ax, (col, ylab, title) in zip(axes, panels):
+        for K in Ks:
+            g = (last[last["K"] == K].groupby("breadth")[col]
+                 .agg(["mean", "sem"]).reset_index())
+            ax.errorbar(g["breadth"], g["mean"], yerr=1.96 * g["sem"].fillna(0.0),
+                        marker="o", lw=1.8, capsize=2, color=colors[K], label=f"K={K}")
+        ax.set_xscale("log")
+        ax.set(xlabel="mate-pool breadth  (monogamous ← → promiscuous)", ylabel=ylab)
+        ax.set_title(title, fontsize=9)
+        ax.legend(title="ruggedness", frameon=False, fontsize=8)
+
+    fig.tight_layout()
+    letter_axes(fig)
+    savefig(fig, "results/figS13_mating_breadth", "E14")
+
+
+if __name__ == "__main__":
+    main()