/assess-evidence-saturation

name: assess-evidence-saturation
description: "Estimate marginal information gain of new evidence batches relative to the current corpus and classify continuing/near-saturation/saturated."

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

1. Declare the eligible evidence universe, novelty dimensions, and current/prior batch boundaries. 2. Compute new topic, independent-source, condition, or mechanism coverage for each comparable batch. 3. Compare marginal information gain and record the evidence supporting the comparison. 4. 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.

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`