The application of airborne remote sensing during an On-Site Inspection
Bibliographic record
Abstract
<p>The application of airborne remote sensing techniques permitted by the Comprehensive Nuclear‑Test‑Ban Treaty (magnetic and gamma survey as well as optical imaging including infrared measurements) is done through the prism of inspection team functionality – a logic which applies equally to air and ground-based techniques. Work undertaken over recent years through modelling and practical testing has aimed to better understand the ability of airborne remote sensing techniques to detect relevant observables under different conditions. This has led to the compilation of a concept of operations document that provides guidance on the application of inspection activities during an On-Site Inspection. As well as highlighting the relative merits of each technique, the document also addresses the relative likelihood a particular airborne technique will return relevant information and will avoid the commitment of resources to missions with little likelihood of success.</p><p>The paper also addresses the approaches which have been taken to streamline the acquisition of airborne remotely sensed data through bespoke installations, the identification of optimal data processing routines to facilitate the production of reports and the fusion of airborne data products with other data gathered during an inspection.</p>
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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 | 0.000 | 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) | 0.000 | 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 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".