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_E4.py

@@ -1,78 +0,0 @@
-"""E4 figure: multi-teacher recombination — supply vs realisation.
-
-Three panels tell the honest story: (A) union coverage rises with K_T and decorrelation,
-matching the exact closed form (recombination *supplies* the tail); (B) that supply is
-realised in the pupil only under a union-preserving merge — mean-mixture distillation
-dilutes it away (flat in K_T) while max-merge keeps it; (C) the union-surviving gap.
-Usage:  python figures/plot_E4.py [results/E4]
-"""
-
-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 U_closed(K_T, rho, q):
-    return rho * q + (1 - rho) * (1 - (1 - q) ** K_T)
-
-
-def main(results_dir: str = "results/E4") -> None:
-    df, cfg = load_bundle(results_dir)
-    q = cfg["coverage"]["q"]
-    K_Ts = sorted(df["K_T"].unique())
-    rhos = sorted(df["rho"].unique())
-    g0 = df[df["g"] == 0.0]
-    colors = plt.cm.viridis(np.linspace(0, 0.85, len(K_Ts)))
-
-    fig, axes = plt.subplots(1, 3, figsize=(15, 4.3))
-
-    # Panel A: union coverage vs rho per K_T, with closed-form overlay
-    ax = axes[0]
-    for K, c in zip(K_Ts, colors):
-        sub = g0[g0["K_T"] == K].groupby("rho")["union_coverage"].mean()
-        ax.plot(sub.index, sub.values, "o", color=c, label=f"$K_T$={K}")
-        ax.plot(rhos, [U_closed(K, r, q) for r in rhos], "-", color=c, lw=1)
-    ax.set(xlabel=r"parent correlation $\rho$", ylabel="union tail coverage",
-           title=r"Supply: union matches $U(K_T,\rho,q)$")
-    ax.legend(frameon=False, fontsize=8)
-
-    # Panel B: surviving coverage vs rho per K_T — mean (dashed) vs max (solid)
-    ax = axes[1]
-    for K, c in zip(K_Ts, colors):
-        sub = g0[g0["K_T"] == K].groupby("rho")
-        ax.plot(sub["surviving_max"].mean().index, sub["surviving_max"].mean().values,
-                "-o", color=c, label=f"$K_T$={K}", ms=4)
-        ax.plot(sub["surviving_mean"].mean().index, sub["surviving_mean"].mean().values,
-                "--", color=c, lw=1, alpha=0.7)
-    ax.set(xlabel=r"parent correlation $\rho$", ylabel="surviving tail coverage",
-           title="Realised: max-merge (solid) rises;\nmean-mixture (dashed) stays flat")
-    ax.legend(frameon=False, fontsize=8)
-
-    # Panel C: surviving vs K_T at rho=0, both operators — the recombination benefit
-    ax = axes[2]
-    r0 = g0[g0["rho"] == 0.0]
-    mx = r0.groupby("K_T")["surviving_max"].agg(["mean", "sem"])
-    mn = r0.groupby("K_T")["surviving_mean"].agg(["mean", "sem"])
-    ax.errorbar(mx.index, mx["mean"], yerr=1.96 * mx["sem"], fmt="-o",
-                color="#1f77b4", capsize=3, label="max-merge (union-preserving)")
-    ax.errorbar(mn.index, mn["mean"], yerr=1.96 * mn["sem"], fmt="--s",
-                color="#d62728", capsize=3, label="mean-mixture distillation")
-    ax.set(xlabel="number of parents $K_T$", ylabel="surviving tail coverage",
-           title=r"Benefit needs a union-preserving merge ($\rho=0$)",
-           xticks=K_Ts)
-    ax.legend(frameon=False, fontsize=9)
-
-    fig.tight_layout()
-    letter_axes(fig)
-    savefig(fig, results_dir, "E4")
-
-
-if __name__ == "__main__":
-    main(*sys.argv[1:])