Preparing the Arctic: Optimally Locating Aeronautical Search and Rescue Stations along Canada’s Northwest Passage
Bibliographic record
Abstract
Although historically ice-covered, the Northwest Passage (NWP)—a maritime corridor located in the Canadian Arctic—has been experiencing melting trends in recent decades. Declining sea ice concentrations would lead to improved navigability along the NWP, suggesting promising opportunities for both domestic and international shippers. With vessel traffic expected to rise, and the lack of emergency response resources currently stationed in the region, Canada would be responsible for equipping its North with a search and rescue (SAR) network that is capable of providing relief to the users of its waterways. Since the Royal Canadian Air Force (RCAF) oversees the majority of SAR activities in Canada, the distribution of its response aircraft throughout the Arctic is crucial in the design of a successful response network. To address these concerns, we formulated the location problem as an integer linear program (ILP) that looked to determine optimal sites for aeronautical SAR stations and the allocation of aircraft so that the weighted primary and secondary coverage of demand points was maximized. To do so, we modelled the response capacities of the RCAF's fleet by designing a set of response functions based on each asset's performance specifications. We analyzed 29 arrangements across two cases: one in which the secondary coverage of demand points was optional (Case A), and another in which it was mandatory (Case B). Using six to seven aircraft, our approach led to three arrangements that would best address SAR concerns in the North: Arrangement 7A which was proposed for Case A, Arrangement 6B for Case B, and Arrangement 7B as a compromise of the two.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".