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文档润色流和知识库构建流/claude-scholar-upstream/skills/results-analysis/examples/example-figure-catalog.md

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+# Figure Catalog
+
+## Figure 1 — Main comparison
+- **Filename**: `figures/figure-01-main-comparison.pdf`
+- **Purpose**: Compare WER across Full fine-tuning, Subject Adapter, and Frozen Encoder.
+- **Data source**: `metrics/summary.csv`
+- **Plot type**: Bar + scatter overlay of seed-level points.
+- **Error bars**: 95% CI.
+- **Caption must include**:
+  - metric direction,
+  - number of seeds,
+  - meaning of error bars,
+  - whether significance markers are corrected.
+- **Key observation**: Subject Adapter closes most of the freezing gap.
+- **Interpretation checklist**:
+  - Is the adapter improvement consistent across seeds?
+  - Is the gap practically meaningful, not just statistically significant?
+  - Does the figure support a design decision?
+
+## Figure 2 — Convergence dynamics
+- **Filename**: `figures/figure-02-training-dynamics.pdf`
+- **Purpose**: Compare optimization stability over epochs.
+- **Data source**: `logs/train_curves.csv`
+- **Plot type**: Mean line + uncertainty ribbon.
+- **Caption must include**:
+  - smoothing rule if any,
+  - whether ribbon is std or CI,
+  - training/eval split.
+- **Key observation**: Frozen Encoder shows larger oscillation after epoch 8.
+- **Interpretation checklist**:
+  - Is instability transient or persistent?
+  - Does curve shape match final metric variance?
+  - Does this explain why one method is harder to tune?