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_E9.py

@@ -1,65 +0,0 @@
-"""E9 figure — landscape robustness: when recombination helps, and the outbreeding-depression risk.
-
-The credibility test for the sexual metaphor. E8 used an additive landscape where recombination
-trivially helps; here parents are local optima ("trained models") of a Kauffman NK landscape whose
-ruggedness (epistasis) is tunable. Blindly recombining entangled models breaks co-adapted allele
-blocks and offspring fall *below* the parents — outbreeding depression — worse the more rugged the
-landscape and the higher the recombination rate. With selection (best offspring), a nonzero optimal
-recombination rate re-emerges. Design rule: merge freely when skills are complementary; merge
-sparingly (and always select) when they are entangled.
-
-Two panels: (A) the risk — mean offspring fitness minus best-parent vs recombination rate, one curve
-per ruggedness K (all ≤0, steeper as K grows); (B) with offspring selection — best-of-brood fitness
-vs rate per K, showing an intermediate optimum on rugged landscapes. Reads only the bundle.
-
-Usage:  python figures/plot_E9.py [results/E9]
-"""
-
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-sys.path.insert(0, str(Path(__file__).parent))
-from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
-
-
-def main(results_dir: str = "results/E9") -> None:
-    df, _ = load_bundle(results_dir)
-    Ks = sorted(df["K"].unique())
-    rates = sorted(df["rate"].unique())
-    colors = plt.cm.viridis(np.linspace(0, 0.85, len(Ks)))
-    bp = df.groupby("K")["best_parent"].mean()
-
-    fig, axes = plt.subplots(1, 2, figsize=(13, 5))
-
-    # Panel A: the risk — mean offspring minus best parent vs rate, per K.
-    ax = axes[0]
-    for K, c in zip(Ks, colors):
-        s = df[df["K"] == K].groupby("rate")["mean_offspring"].mean() - bp[K]
-        ax.plot(s.index, s.values, "-o", color=c, ms=4, label=f"K={K}")
-    ax.axhline(0, ls=":", color="gray", lw=1)
-    ax.set(xlabel="recombination rate", ylabel="mean offspring − best parent",
-           title="The risk: outbreeding depression\n(worse with ruggedness K and recombination rate)")
-    ax.legend(frameon=False, fontsize=8, title="ruggedness")
-
-    # Panel B: with selection — best offspring vs rate, per K (intermediate optimum on rugged).
-    ax = axes[1]
-    for K, c in zip(Ks, colors):
-        s = df[df["K"] == K].groupby("rate")["best_offspring"].mean()
-        ax.plot(s.index, s.values, "-o", color=c, ms=4, label=f"K={K}")
-        ax.axhline(bp[K], ls=":", color=c, lw=0.8, alpha=0.6)
-    ax.set(xlabel="recombination rate", ylabel="best-of-brood fitness (with selection)",
-           title="With offspring selection, an optimal\nrecombination rate re-emerges (dotted = parents)")
-    ax.legend(frameon=False, fontsize=8, title="ruggedness")
-
-    fig.tight_layout()
-    letter_axes(fig)
-    savefig(fig, results_dir, "E9")
-
-
-if __name__ == "__main__":
-    main(*sys.argv[1:])