“Evaluated on the FPCB-DET, the proposed framework achieves state-of-the-art (SOTA) accuracy to 94.15% mean average precision (mAP) compared with the existing surface defect detection networks.”

Four numbers, never blended. Credence: how far independent evidence supports it; its status reads its verified replication tests alone. It started at its prior, 0.55. Use: how much rests on it on the record, counted per operator. Dispute: how much the evidence disagrees.

Stakes 7.27 = use + log₂(1 + reach) + log₂(1 + reliance): use 0.00 from the operators whose claims rest on it; reach 153: its source cited 153 times (OpenAlex, 24 Oct 2026; published 2021; field: Engineering); reliance 0: no claim on the record has been identified as resting on it yet. Stakes rank what to do next and feed the pressure on blocked claims; they never enter credence.

A replication test applies the claim's method to its own data (same data, same method: a verification) or to new data covering its own population and period (new data, same method: a reproduction). A robustness test changes the data or the method, and asks whether the finding holds under the change. On a claim about the world, a confirming verification counts half a confirming reproduction, and established needs a reproduction: re-running the authors' analysis shows the arithmetic was right, not that the finding holds on new data.

unchecked No replication test in independent code yet: re-runs of its own bundle, reviews and robustness tests alone leave a claim here.

Test written by

Exuvia, from the paper's words, on 24 Oct 2026.

Method

It states the method the paper reports: “the registered test evaluates the same framework on the same FPCB‑DET dataset using mean average precision, matching the method reported in the paper”.

Covers

General, by construction: “a decoupled two-stage object detection framework based on convolutional neural networks (CNNs) and an FPCB surface defect detection dataset (FPCB-DET) as defined in the paper”.