Risk Modelling for Remote Communities: An Inuit-driven Bayesian Network Approach to Enhance Search and Rescue Operations in Arctic Canada
Bibliographic record
Abstract
The Canadian Arctic's vast and unforgiving landscape presents unique challenges for Search and Rescue (SAR) operations, particularly in supporting the SAR volunteers in remote Inuit communities who face extreme conditions with limited resources. This study addresses the need for a culturally informed, probabilistic model that can enhance SAR effectiveness in Nunavut and Nunavik by enabling data-driven strategic planning and resource allocation. The paper introduces a novel Bayesian Network (BN) risk model that aims to capture the complexities involved in the Arctic ground SAR system. The model, developed through extensive community engagement, highlights the interdependencies between environmental conditions, resource availability, and SAR outcomes. By incorporating local knowledge and addressing systemic risks, the BN model offers a quantitative framework for SAR decision-making and policy development, aiming to improve the safety and resilience of Northern communities in the face of climate change and evolving geopolitical challenges. This work contributes to the wider SAR literature by offering a replicable approach for risk assessment and decision-making rooted in community expertise.
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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 | 0.001 | 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".