wiki-query
PApaperclipai/paperclip102k近 7 天新增 3,122 个 star
assess-evidence-saturation
Estimate marginal information gain of new evidence batches relative to the current corpus and classify continuing/near-saturation/saturated.
- Star
- 503
- 分类
- 科研
- 许可证
- Apache-2.0
- 最近提交
- 2026年9月29日
skills/assess-evidence-saturation/SKILL.md
在 GitHub 上查看assess-evidence-saturation
Purpose
Compare the marginal information gain of a new evidence batch with the comparable prior batch and classify acquisition as continuing, near-saturation, or saturated.
Input contract
required: [current_corpus, prior_comparable_batch, novelty_schema]
optional: [topic_schema, quality_weights, stopping_policy]
constraints: [batches must share a declared universe and comparable novelty calculation]
Procedure
- Declare the eligible evidence universe, novelty dimensions, and current/prior batch boundaries.
- Compute new topic, independent-source, condition, or mechanism coverage for each comparable batch.
- Compare marginal information gain and record the evidence supporting the comparison.
- Classify continuing, near-saturation, or saturated and state the stopping rationale.
Output contract
produces: [declared_universe, batch_comparison, marginal_gain, saturation_state, stopping_rationale]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
Quality gates
- Numerator, denominator, batch increment, source references, direction, and rationale are reproducible.
- The comparison uses the same novelty schema for both batches.
- Saturation is not declared from corpus size alone.
- Saturation describes acquisition only: it states that further retrieval adds little, never that the question the evidence addresses is answered.
Failure and counterexamples
Do not compare incomparable batches, count duplicate sources as new information, or treat a low-quality batch as evidence of saturation.
Provenance map
resolved: knowledge-acquisition-saturation-detection
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