Identify the question and tested system
Determine exactly what the study measured: classification, similarity, localisation, continuity, provenance or another task. Record the implementation and version. A published method, a local test and an independently reproduced validation are different evidence statements.
Check the population and evidence conditions
Look at writer count and scripts, devices and cameras, recording environments, capture quality and transformations. Test cases should represent the intended work, including realistic difficult negatives. A small synthetic test cannot establish performance across real-world casework.
Understand the evaluation design
Look for separate training and testing material and appropriate writer, device or source separation. Ask how thresholds were selected and whether results used held-out data. Repeated crops, shared source files or closely related samples can make a test look more convincing than it is.
Read more than one performance measure
False-positive and false-negative outcomes, inconclusive rates, uncertainty, localisation performance and failures outside scope can all matter. A single accuracy percentage can hide an unsuitable threshold or unbalanced test set. Repeatability is useful but does not establish correctness.
Assess the combined human workflow
Evaluate preparation, processing, examiner review, corrections and reporting together. A quick automated run can still create review burden. Test whether explanations lead reviewers to over-trust outputs or overlook conflicting evidence. Measure time and quality against a comparable baseline.
Document the boundary
Record what is established, preliminary or unassessed. Explain relevant differences between the test and the submitted evidence. Do not report benchmark performance as a probability that this particular result is correct. The research and validation page records supplied scope notes; it does not independently validate every listed entry.