Effective, Efficient, and Environmentally Friendly Out-of-Model-Scope Detection Methodology
Bibliographic record
Abstract
Integrating deep neural networks (DNNs) in safety-critical systems is widespread, but their reliability depends on accurate performance in real-world environments. Capturing all scenarios in training data is impractical. One solution is to use DNNs within their known range and alert a human operator when encountering unreliable outputs, i.e., out-of-model-scope (OMS) outputs. However, current unsupervised OMS detection methods monitor all neurons, are computationally expensive, and are not robust enough to avoid neuron noises. In this paper, we propose an effective, efficient, and environmentally friendly methodology, EFOMS, that automatically filters unreliable outputs by extending existing OMS detection methods to focus only on significant neurons, thereby filtering out noise from unimportant neurons. EFOMS achieves comparable or better OMS detection quality with significantly reduced computational costs: 45% faster, consuming 30% less energy, producing 30% fewer carbon emissions, and using up to 21% less peak memory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".