Bibliographic record
Abstract
Identifying bad objects hidden amidst many good objects is important for public safety and decision-making. These problems are complicated in that the cost of leaving a bad object unidentified may not be specified easily, making it difficult to apply existing cost-sensitive classification that depends on knowing a cost matrix or cost distribution. A compelling case for this "illusive cost" issue is presented in our project of identifying contaminated transformers with an industrial partner. To address this problem, we present an alternative formulation of cost-sensitive classification, Clearance Under Threshold (CUT) Classification. Given a training set, CUT classification is to partition the attribute space such that a partition is cleared if the probability of a future object in this partition being bad is less than a user-specified threshold. The goal is to clear many low-risk objects so that users can more effectively target high-risk objects. We present a solution to this problem and evaluate it on a case study for clearing contaminated transformers and on public benchmarks from UC Irvine's Machine Learning Repository. According to the experiments, our algorithms performed far better than the baselines derived from previous classification approaches.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".