Designing a Bayesian Urgency Assessment Tool for Search and Rescue in the Canadian Arctic
Bibliographic record
Abstract
Effective urgency assessment is critical for search and rescue (SAR) operations, particularly in remote regions such as the Canadian Arctic. Vast distances, severe weather, and limited resources present significant challenges in Nunavut and Nunavik. Existing urgency assessment frameworks, while effective in other contexts, are often unsuitable for Arctic ground SAR. This paper reviews existing urgency assessment frameworks, including SAR-specific systems and Bayesian network (BN) approaches, assessing their applicability to the Arctic context. It further explores the potential for developing a BN-based urgency assessment tool tailored to ground SAR in Nunavut and Nunavik.We discuss key factors that such a model might incorporate—such as environmental conditions, shelter availability, and local knowledge—and highlight the benefits of probabilistic reasoning in supporting decision-making and optimising resource allocation. While a fully realised prototype is not yet presented, this research outlines the conceptual groundwork for future development. The ultimate aim is to improve decision support for SAR coordinators, risk communication, and the overall effectiveness of Arctic SAR efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".