Community-led non-invasive polar bear monitoring in the Eeyou Marine Region of James Bay, Canada: insights on distribution and body condition during the ice-free season
Bibliographic record
Abstract
Wildlife conservation is informed by detailed understanding of species demographics, habitat use, and interactions with environmental drivers. Challenges to collecting this information, particularly in remote places and on widely ranging species, can contribute to data deficiencies that detract from conservation status assessment and the effectiveness of management actions. Polar bears in James Bay face rapidly changing environmental conditions at the southern edge of their global range, but studying their ecology has been limited by community concerns about the methods typically used in polar bear research. Using a community-led and non-invasive approach, we deployed hair snare and camera trap sampling stations across 400 km of the Eeyou Marine Region in eastern James Bay. Stations collected >100 hair samples and thousands of photographs in one eight-week period that allowed for a novel investigation of this population’s distribution and body condition during the ice-free season. Polar bears were in average to above average body condition, and model selection of detections at stations revealed distance to mainland as a significant predictor of polar bear presence. Given its high potential, we suggest community-based monitoring using this method become a standard protocol to expand the scope and local leadership of polar bear research across the North.
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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 | 0.001 |
| 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".