backup materials and knowledge-base docs

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

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+# Strict Analysis Report
+
+## Analysis Question
+- Compare Full Model, Frozen Encoder, and Subject Adapter on cross-subject decoding.
+- Determine whether the adapter closes the transfer gap without sacrificing stability.
+
+## Data Inventory
+- 3 model families
+- 5 random seeds per family
+- subject-level WER and CER
+- training logs for convergence
+- ablation outputs for adapter width
+
+## Executive Summary
+- Subject Adapter improves mean WER over Frozen Encoder by 3.8 absolute points.
+- The improvement is statistically significant under paired testing after multiple-comparison correction.
+- Full fine-tuning remains strongest overall, but Subject Adapter offers the best compute-performance tradeoff.
+- Performance variance is lower than Frozen Encoder, suggesting a more stable transfer path.
+
+## Main Findings
+
+### 1. Main comparison
+- Full fine-tuning: `31.4 ± 1.9` WER
+- Subject Adapter: `33.2 ± 1.3` WER
+- Frozen Encoder: `37.0 ± 2.1` WER
+
+Observation:
+- Subject Adapter consistently outperforms Frozen Encoder across all 5 seeds.
+
+Interpretation:
+- The transfer bottleneck is not purely feature reuse; a light adaptation head captures subject-specific alignment that freezing alone misses.
+
+Implication:
+- Future work should prioritize adapter variants before investing in heavier full-model tuning.
+
+### 2. Stability
+- Subject Adapter has the smallest seed variance among transfer-friendly methods.
+- Convergence curves show less oscillation after epoch 8.
+
+Interpretation:
+- Adapter tuning is not only better on average, but easier to optimize.
+
+### 3. Ablation
+- Reducing adapter width from 256 to 64 hurts WER by 1.6 points.
+- Increasing from 256 to 512 yields only marginal improvement.
+
+Interpretation:
+- Most of the benefit is captured at moderate width; scale-up is not the current bottleneck.
+
+## Caveats
+- Only 5 seeds; inference about tails remains limited.
+- Subject count is modest, so subject-level heterogeneity may still be under-estimated.
+- Training-time comparison depends on identical hardware assumptions.
+
+## Recommended Next Move
+- Promote this finding into a `results-report` note focused on adapter vs freezing.
+- Run one robustness pass on held-out low-resource subjects.