The role of environmental factors in search and rescue incidents in Nunavut, Canada
Bibliographic record
Abstract
OBJECTIVES: Unintentional injury is a leading cause of morbidity and mortality in Nunavut, where the importance of land-based activities and reliance on semi-permanent trails create unique risk profiles. Climate change is believed to be exacerbating these risks, although no studies have quantitatively examined links between environmental conditions and injury and distress in the Canadian Arctic. We examine the correlation between environmental conditions and land-based search and rescue (SAR) incidents across Nunavut. STUDY DESIGN: Case study. METHODS: Case data were acquired from the Canadian National Search and Rescue Secretariat. Gasoline sales from across the territory are then used to model land-use and exposure. We compare weather and ice conditions during 202 SAR incidents to conditions during 755 non-SAR days (controls) between 2013 and 2014. RESULTS: We show daily ambient temperature, ice concentration, ice thickness, and variation in types of ice to be correlated with SAR rates across the territory during the study period. CONCLUSIONS: These conditions are projected to be affected by future climate change, which could increase demand for SAR and increase injury rates in the absence of targeted efforts aimed at prevention and treatment. This study provides health practitioners and public health communities with clearer understanding to prepare, respond to, and prevent injuries across the Arctic.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".