Motivating the inclusion of meteorological indicators in the CTBT feature-space
Bibliographic record
Abstract
Verification of the Comprehensive Test-Ban-Treaty (CTBT), as a Pattern Recognition (PR) problem, has been proposed based on four radioxenon features. It has been noted, however, that in many cases this limited feature set is insufficient to distinguish radioxenon levels effected by an explosion from those that are solely products of industrial activities. As a means of improving the detectability of low-yield clandestine nuclear explosions, this paper motivates the inclusion of meteorological indicators in the CTBT feature-space, promotes further research into which meteorological indicators are most informative, and how they may be acquired. In doing so, we present classification results from four simulated scenarios. These results demonstrate that the inclusion of a simple wind direction feature can significantly increase the prospect of classifying challenging detonation events, and suggests the predictive power of meteorological features in general.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".