Incorporating Uncertainty in Unexploded Ordnance Discrimination
Why this work is in the frame
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Bibliographic record
Abstract
We examine representations of feature vector uncertainty in the context of unexploded ordnance (UXO) discrimination with electromagnetic data. We compare a local uncertainty estimate derived from the curvature of the misfit function with global estimates of the model posterior probability density (PPD) obtained with Markov chain sampling. For well-posed experiments (i.e., with high SNR and adequate spatial coverage), the two methods of uncertainty appraisal agree. However, when the inverse problem is ill posed, we find out that the PPD can be multimodal. To incorporate these uncertainties in discrimination, we first develop an extension of discriminant analysis which integrates over the posterior distribution of the model. When dealing with multimodal PPDs, we show that an effective solution is to input all modes of the PPD-corresponding to all models at local minima of the misfit-into discrimination and, then, to classify on the basis of the model which is most likely a UXO.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it