docs: correct scene-boundary detector F1 numbers to measured values

The boundary-detection paragraph understated the detector. Replace the stale
"~34% F1 vs ~27%" (a pre-C++-retrain figure with no backing artifact) with the
measured numbers at the shipped ±20s tolerance:
  - leave-one-out macro boundary F1 = 44.1% (honest generalisation)
  - grayscale baseline               = 29.8%
  - train-all (shipped model)        = 72.9% (per-film 51-86%)
computed from experiments/results/scene_boundary/xgb_report.json and per-film
leave-one-out runs of train_xgb_cpp.py. Also correct the false claim that the
low-contrast grades "cannot generalise held out" — Scarface held out scores 32%,
Café Society 51%, both above grayscale (0% and 31%).

Split the evolution figure into two panels so the strict-±2s feature-development
curve is no longer mistaken for the shipped result: left = feature progress at
±2s, right = shipped detector at the ±20s tolerance the pipeline uses.

scripts/scene_detector/make_figures.py

@@ -57,15 +57,34 @@ def fig_macro():
     fig.tight_layout(); fig.savefig(OUT/"scene_presence_macro.png"); plt.close(fig)
 
 # ── Figure 3: feature/model evolution (boundary-F1 development) ──────────────
+# Two panels, because the development curve and the shipped result are measured
+# at DIFFERENT tolerances and must not be plotted on one axis:
+#   left  — relative feature progress at the strict ±2 s tolerance (how the LSTM
+#           experiments were scored; establishes which features helped)
+#   right — the shipped XGBoost detector at the ±20 s tolerance the pipeline
+#           actually uses and scores at (grayscale vs learned-LOO vs train-all)
 def fig_evolution():
+    fig,(axl,axr)=plt.subplots(1,2,figsize=(11,4.5),gridspec_kw={"width_ratios":[1.15,1]})
+
     steps=["grayscale\nbaseline","raw-hist\nLSTM","delta\nLSTM","XGBoost\n(delta+debounce)"]
-    f1=[7.2,7.5,10.8,15.2]   # boundary-F1 @±2s during development
-    fig,ax=plt.subplots(figsize=(6.5,4.5))
-    ax.plot(steps,f1,marker="o",color="#3d7ea6",lw=2,ms=8)
-    for i,v in enumerate(f1): ax.text(i,v+0.4,f"{v:.1f}%",ha="center",fontsize=10)
-    ax.set_ylabel("held-out boundary F1 @±2s (%)")
-    ax.set_title("Detector development: features + model")
-    ax.set_ylim(0,18)
+    dev=[7.2,7.5,10.8,15.2]   # boundary-F1 @±2s during LSTM-era development
+    axl.plot(steps,dev,marker="o",color="#9aa7b4",lw=2,ms=8)
+    for i,v in enumerate(dev): axl.text(i,v+0.4,f"{v:.1f}%",ha="center",fontsize=9)
+    axl.set_ylabel("boundary F1 @±2 s (%)")
+    axl.set_title("Feature progress (strict ±2 s)")
+    axl.set_ylim(0,18)
+
+    # shipped detector at the ±20s tolerance the pipeline uses — real measured
+    # macro numbers: grayscale (xgb_report gray_F1), learned LOO, learned train-all
+    names=["grayscale","learned\n(LOO)","learned\n(train-all)"]
+    f20=[29.8,44.1,72.9]; cols=["#e07a5f","#3d7ea6","#8fb8cf"]
+    bars=axr.bar(names,f20,color=cols)
+    for b,v in zip(bars,f20): axr.text(b.get_x()+b.get_width()/2,v+1.2,f"{v:.1f}%",
+                                       ha="center",fontsize=10,fontweight="bold")
+    axr.set_ylabel("boundary F1 @±20 s (%)")
+    axr.set_title("Shipped detector (±20 s, macro/9 films)")
+    axr.set_ylim(0,80)
+    fig.suptitle("Detector development, and where it landed",fontsize=13)
     fig.tight_layout(); fig.savefig(OUT/"scene_detector_evolution.png"); plt.close(fig)
 
 import csv as _csv