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_figS5_aimed_grounding.py

@@ -0,0 +1,73 @@
+"""E3 figure: region-matched grounding.
+
+Shows that grounding must *overlap* the content it protects. At the same total budget,
+uniform grounding spreads thin and lets the exercised region's tail collapse, while
+matched grounding concentrates on that region and keeps its rare items alive (at the cost
+of the regions it does not touch). Usage:  python figures/plot_figS5_aimed_grounding.py [results/figS5_aimed_grounding]
+
+Metric: per-region tail-item survival. (Per-region *heterozygosity* is confounded by
+region mass under matched grounding, so it is deliberately not used here.)
+"""
+
+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/figS5_aimed_grounding") -> None:
+    df, cfg = load_bundle(results_dir)
+    R = cfg["truth"]["R"]
+    exercised = cfg["dynamics"]["grounding"]["exercised"]
+    target = exercised[0]
+    last = int(cfg["generations"] * 0.8)
+    colors = {"uniform": "#d62728", "matched": "#1f77b4"}
+
+    fig, axes = plt.subplots(1, 2, figsize=(12, 4.4))
+
+    # Panel 1: tail survival of the target region over generations
+    ax = axes[0]
+    tcol = f"tailalive_region_{target}"
+    for pol in ("uniform", "matched"):
+        sub = df[df["policy"] == pol].groupby("generation")[tcol]
+        mean = sub.mean()
+        sem = sub.sem()
+        ax.plot(mean.index, mean.values, color=colors[pol], label=pol)
+        ax.fill_between(mean.index, mean - 1.96 * sem, mean + 1.96 * sem,
+                        color=colors[pol], alpha=0.2)
+    ax.set(xlabel="generation",
+           ylabel=f"tail items alive in region {target}",
+           title=f"Target region {target} (exercised):\nmatched holds, uniform collapses")
+    ax.legend(frameon=False)
+
+    # Panel 2: stationary tail survival per region, uniform vs matched
+    ax = axes[1]
+    stat = df[df["generation"] >= last]
+    regions = np.arange(R)
+    width = 0.4
+    for i, pol in enumerate(("uniform", "matched")):
+        vals = [stat[stat["policy"] == pol][f"tailalive_region_{r}"].mean()
+                for r in regions]
+        ax.bar(regions + (i - 0.5) * width, vals, width,
+               color=colors[pol], label=pol)
+    ax.axvline(target, ls=":", color="gray", lw=1)
+    ax.annotate("exercised", (target, ax.get_ylim()[1] * 0.9), fontsize=8,
+                ha="center", color="gray")
+    ax.set(xlabel="region", ylabel="stationary tail items alive",
+           title="Uniform spreads thin;\nmatched concentrates on the exercised region",
+           xticks=regions)
+    ax.legend(frameon=False)
+
+    fig.tight_layout()
+    letter_axes(fig)
+    savefig(fig, results_dir, "figS5_aimed_grounding")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])