A Network-based Simulation Model for Helicopter Rescue Time Estimation in the Canadian Arctic
Bibliographic record
Abstract
Search-and-rescue (SAR) helicopter operations in Arctic areas with limited infrastructure are crucial for saving lives under harsh weather conditions. This study develops a SAR helicopter response model that first identifies all possible paths for a rescue mission using path network optimization, then quantifies the effects of weather on travel time for each route and finally employs discrete-event and Monte Carlo simulation to evaluate the performance of SAR missions. The approach leverages meteorological data to classify favorable, unfavorable, or no-go conditions for SAR helicopter operability, offering insights into travel and rescue times from SAR bases to people in distress across the Canadian Arctic. Preliminary findings highlight how route selection, weather severity, and refueling constraints influence mission durations. While further work is needed to develop, test, and validate the model, these results indicate the model’s promise to enhance SAR planning, reinforcing community resilience under harsh, changing Arctic environments.
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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".