Assessing Drone Performance and Probability of Detection for Search and Rescue Operation in Northern Canada
Bibliographic record
Abstract
Drones are increasingly valuable in Search and Rescue (SAR) operations, offering rapid aerial coverage and improved situational awareness in remote environments. However, environmental constraints and operational limitations affect their effectiveness. This study evaluates drone deployability and operation in SAR missions across Northern Canada through statistical analysis of SAR operability in various weather conditions, accounting for seasonal variations, and Probability of Detection (PoD) in different search scenarios. The first research question classifies operability categories based on historical weather data, identifying periods and locations for reliable drone deployment, considering factors like temperature, wind speed, and seasonal changes. The second research question estimates PoD, assessing the likelihood of detecting Persons in Distress (PiDs) with drones under varying conditions, including terrain, sensor capabilities, and target visibility. This research aims to evaluate drone usefulness in SAR operations by identifying constraints, informing deployment planning, and supporting technological advancements to improve search effectiveness in remote, high-risk areas.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".