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

tasks/workorder-E2-analysis-addons.md

@@ -34,7 +34,7 @@ tail metric showing the deep band stays dead while the shallow band recovers —
 
 ---
 
-## 1. New module: `src/knowledge/analysis.py`
+## 1. New module: `src/inheritance/analysis.py`
 
 Post-hoc analysis of E2 results. Pure NumPy/pandas, seeded, deterministic.
 
@@ -109,7 +109,7 @@ def critical_grounding(stationary_df, H_star, frac=0.95, sweep_col="g",
 
 ---
 
-## 2. New online metric in `src/knowledge/metrics.py` (optional band panel)
+## 2. New online metric in `src/inheritance/metrics.py` (optional band panel)
 
 Compute this each generation from the current `p` and log the per-band arrays exactly
 like the existing per-region metrics (e.g. columns `tail_frac_alive_band{b}` and
@@ -143,7 +143,7 @@ def tail_band_metrics(p, p_star, tail_mask, n_bands=4, alive_eps=1e-9):
 
 ---
 
-## 3. Figure updates: `figures/plot_E2.py`
+## 3. Figure updates: `figures/plot_fig2_grounding_sweep.py`
 
 - **Middle panel:** call `critical_grounding(reduce_to_stationary(df), H_star, frac=0.95)`
   and draw a vertical line/marker at `g_star` with a shaded CI band; annotate
@@ -164,8 +164,8 @@ def tail_band_metrics(p, p_star, tail_mask, n_bands=4, alive_eps=1e-9):
 
 ```python
 import numpy as np, pandas as pd, pytest
-from knowledge.analysis import reduce_to_stationary, critical_grounding
-from knowledge.metrics import tail_band_metrics
+from inheritance.analysis import reduce_to_stationary, critical_grounding
+from inheritance.metrics import tail_band_metrics
 
 
 def _H_eq(n, m, Hs): return Hs * m * (2 * n + m - 1) / (n + 2 * n * m + m * m)