PATHA: A Planning Aid for Tasking Heterogeneous Assets for route survey or mine countermeasures operations
Bibliographic record
Abstract
The Planning Aid for Tasking Heterogeneous Assets (PATHA) is a decision support tool that addresses shortcomings in current route survey or naval mine countermeasures mission planning capabilities. The tool is able to optimally assign heterogeneous assets to segments in an area of interest. Track positions for each allocation are also determined by PATHA, in order to give optimal coverage in each segment. These assignments are calculated using each asset's sonar performance-based on environmental characteristics of the ocean-and the time taken for the asset to clear the segment. The sonar performance modelling tool, ESPRESSO, is used to infer sonar performance curves based on sonar properties and environmental characteristics. These models use a novel combination of Signal-to-Noise and Shadow-to-Background ratios as a function of target range and sonar altitude to qualify sonar performance. In this paper, we show how PATHA can be used to plan a complex mission involving many assets with diverse performances in a large oceanographic area with heterogeneous seafloor. We also show how PATHA is easily adaptable to changes in mission parameters (e.g. number of assets, constraints on clearances or time, et cetera). This allows for comprehensive mission analysis of “What If” scenarios and real-time mission considerations.
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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".