Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles
Bibliographic record
Abstract
This chapter focuses on two key metrics that characterize the efficiency and effectiveness of autonomous underwater vehicles (AUVs) in such operations. The first metric is the confidence that mines are or are not present in the search area. The second is the time required to achieve that confidence level. It is assumed that the AUV carries a side-scan sonar. The mine-hunting environment plays an important role in the performance of side-scan sonar, as it does for any mine countermeasures (MCM) sensor. The chapter examines search patterns, including lawn-mowing, zigzagging, and random searches. The measures of effectiveness (MOEs) of the first two patterns were evaluated through the use of a stochastic model and a deterministic one, whose outcomes were verified to be consistent. Based on the assumed measures of performance (MOPs), the chapter demonstrates that the 2MU search pattern provides the best probability of detection as a function of search time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".