Notebooks todos sem resultados fase 4

classifier/notebooks/03_phase1_analysis.ipynb

@@ -818,22 +818,12 @@
     "The confusion matrices show the AUC story in error-count form. SimpleCNN correctly classifies about `71%` of real images and `70%` of fake images, so it misses many examples in both directions. ResNet18 improves both sides: about `81%` of real images are kept real, and about `88%` of fake images are detected as fake. The most important practical gain is fewer fake images predicted as real (`30%` -> `12%`), although the model still produces some false alarms on real images (`19%`).\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "0591bcea",
-   "metadata": {},
-   "source": [
-    "## 6. Reproducibility note\n",
-    "\n",
-    "These checks are not extra results. They simply support the credibility of the Phase 1 comparison: the same config-loading rules, grouped splits, deterministic evaluation transforms, and safe metric handling are covered by tests in `classifier/tests/`. This lets the baseline comparison focus on the intended difference: SimpleCNN versus pretrained ResNet18.\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "624839d8",
    "metadata": {},
    "source": [
-    "## Report-ready conclusion\n",
+    "## Conclusion\n",
     "\n",
     "Under identical Phase 1 conditions, ResNet18 is the stronger baseline. SimpleCNN reaches AUC `0.7786`, accuracy `0.7039`, and F1 `0.7801`. ResNet18 reaches AUC `0.9366`, accuracy `0.8650`, and F1 `0.9073`. The mean fold-wise AUC improvement is about `+0.1580`.\n",
     "\n",