force recovery: methods, evaluation, debug log

results.html

@@ -73,15 +73,15 @@ each row, and differences between panels are differences between methods.</p>
 <th>FeelAnyForce<br>(trained: markerless)</th></tr>
 <tr><td>FEATS val (marker)</td><td>0.74</td>
 <td class="best">0.96 · in-domain</td><td class="dead">0.43</td></tr>
-<tr><td>FoTa cnc_Mini (markerless)</td><td>0.43 (0.65 non-edge)</td>
+<tr><td>FoTa cnc_Mini (markerless)</td><td>0.94 (in view)</td>
 <td class="dead">0.07</td><td class="best">0.83</td></tr>
-<tr><td>GlowTact (markerless)</td><td>0.63</td>
+<tr><td>GlowTact (markerless)</td><td>0.99</td>
 <td class="dead">0.04</td><td class="best">0.90</td></tr>
 </table>
 <p>The pattern is the finding: <b>each network dominates its own gel domain and
 collapses outside it</b> (FEATS 0.96 → 0.04–0.07; FeelAnyForce 0.90 → 0.43),
 while <b>the physics pipeline is the only estimator that works everywhere</b>
-(0.43–0.74) — it never sees training data, so it has no domain to leave.</p>
+(0.74–0.99) — it never sees training data, so it has no domain to leave.</p>
 </div>
 
 <h2>FEATS dataset — marker-dot gel</h2>
@@ -94,8 +94,9 @@ ways.</p></div>
 <h2>FoTa cnc_Mini — markerless gel</h2>
 <div class="card"><img src="assets/results_cnc.png" alt="cnc_Mini panels">
 <p class="footnote">Hard conditions: only 4 contact-free frames, 62% of presses
-near the pad border. Our edge-filtered ρ is 0.65; FeelAnyForce reaches 0.92
-non-edge.</p></div>
+near the pad border — the press grid is larger than the field of view (see
+<a href="debug_pipeline.html">pipeline debug</a>). Strictly in view our ρ is
+0.94 (MAE 0.26 N); FeelAnyForce reaches 0.92 non-edge.</p></div>
 
 <h2>GlowTact — markerless gel, cleaned</h2>
 <div class="card"><img src="assets/results_glowtact.png" alt="GlowTact panels">

results_zh.html

@@ -72,14 +72,14 @@ th,td{white-space:normal;min-width:110px}}
 <th>FeelAnyForce<br>(训练域:无点)</th></tr>
 <tr><td>FEATS val(有点)</td><td>0.74</td>
 <td class="best">0.96 · 本域</td><td class="dead">0.43</td></tr>
-<tr><td>FoTa cnc_Mini(无点)</td><td>0.43(非边缘 0.65)</td>
+<tr><td>FoTa cnc_Mini(无点)</td><td>0.94 (视野内)</td>
 <td class="dead">0.07</td><td class="best">0.83</td></tr>
-<tr><td>GlowTact(无点)</td><td>0.63</td>
+<tr><td>GlowTact(无点)</td><td>0.99</td>
 <td class="dead">0.04</td><td class="best">0.90</td></tr>
 </table>
 <p>规律本身就是发现:<b>每个网络只统治自己的 gel 域,出域即塌</b>
 (FEATS 0.96 → 0.04–0.07;FeelAnyForce 0.90 → 0.43);
-而<b>物理管线是唯一在所有域都工作的估计器</b>(0.43–0.74)——
+而<b>物理管线是唯一在所有域都工作的估计器</b>(0.74–0.99)——
 它没见过训练数据,所以也没有可以离开的域。</p>
 </div>
 
@@ -90,7 +90,7 @@ th,td{white-space:normal;min-width:110px}}
 
 <h2>FoTa cnc_Mini——无点 gel</h2>
 <div class="card"><img src="assets/results_cnc.png" alt="cnc_Mini panels">
-<p class="footnote">条件苛刻:只有 4 帧无接触,62% 的按压贴边。我们边缘过滤后 ρ=0.65;
+<p class="footnote">条件苛刻:只有 4 帧无接触,62% 的按压贴边。我们严格视野内 ρ=0.94(MAE 0.26 N,见管线调试页);
 FeelAnyForce 非边缘达 0.92。</p></div>
 
 <h2>GlowTact——无点 gel,清洗过</h2>