Entropic skill assessment of unmanned aerial systems (UAS) operators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large-scale distributed training exercises involve many trainees at various stages of their training maturity and at various levels of skill. Problems arise in large-scale exercises when less mature or lower-skilled trainees are exposed to training scenarios that are too advanced or too complex for their level of training maturity. These trainees are more likely to fail the mission they are given in the training scenario, thus reducing the benefits of training, leading to frustration in the trainee or even disrupting the training exercise. We present a methodology for automated skill assessment using entropy measures that form the core of a battery of automated assessment algorithms. As illustrated in a case study, in which subjects performed a reconnaissance task in a simulated unmanned aerial system environment, this methodology achieves high accuracy levels of skill assessment and has the added benefit of computational simplicity, allowing for real-time skill assessment of trainees.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it