A multiobjective coverage‐based model for Civilian search and rescue
Bibliographic record
Abstract
Abstract The Civil Air Search and Rescue Association (CASARA) is a Canada‐wide volunteer aviation association that provides air search support services to the Canadian National Search and Rescue (SAR) program. As with any emergency service provider, the locations of CASARA units greatly impact their overall effectiveness. In this article, the optimal location of CASARA units is formulated as a multiobjective maximal covering location problem. The model addresses the objectives of maximizing the coverage, minimizing the number of units, and maximizing the backup coverage of SAR incidents within Canada. A multigender genetic algorithm is proposed to determine a set of nondominated CASARA location configurations. Results are compared with solutions found using commercial integer programming software. It is shown that the nondominated genetic algorithm solutions are near‐optimal. These are determined in much less time than comparable solutions using commercial integer programming software. © 2009 Wiley Periodicals, Inc.* Naval Research Logistics, 2011
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".