Bibliographic record
Abstract
Abstract Fault clustering attempts to partition a set of faulty samples into several clusters, allowing the exploration of the underlying pattern of faults. Nonnegative matrix factorizations (NMFs) are good candidates for fault clustering since they are inherently capable of data clustering and variants of the ‐means algorithm. However, NMFs always show poor performance in real‐world clustering applications for their naive data clustering mechanism. To improve the clustering performance of the existing NMFs and solve the fault clustering problem, this paper proposes a new type of NMFs, called small‐entropy nonnegative matrix factorizations (SENMFs). SENMFs impose a small amount of entropy on the cluster probability distribution of each sample to avoid ambiguous clustering results. Moreover, the algorithm for SENMFs is convergent in theory. We selected three types of faulty samples of the penicillin fermentation process for fault clustering. The case study results showed that SENMFs exceed the state‐of‐the‐art NMFs and ‐means in terms of fault clustering performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".