Migration dynamics of polar bears (<i>Ursus maritimus</i>) in western Hudson Bay
Bibliographic record
Abstract
Abstract Migration is predicted to change both spatially and temporally as climate change alters seasonal resource availability. Species in extreme environments are especially susceptible to climate change; hence, it is important to determine environmental and biological variables that influence their migration. Polar bears (Ursus maritimus) are an Arctic apex carnivore whose migration phenology has been affected by climate change and is vulnerable to future changes. Here, we used satellite-linked telemetry collar data from adult female polar bears in western Hudson Bay from 2004 to 2016 and multivariate response regression models to demonstrate that 1) spatial and temporal migration metrics are correlated, 2) ice concentration and wind are important environmental variables that influence polar bear migration in seasonal ice areas, and 3) migration did not vary across the years of our study, highlighting the importance of continued monitoring. Specifically, we found that ice concentration, wind speed, and wind direction affected polar bear migration onto ice during freeze-up and ice concentration and wind direction affected migration onto land during breakup. Bears departed from land earlier with increased wind speed and the effect of wind direction on migration may be linked to prey searching and ice drift. Low ice concentration was associated with higher movement during freeze-up and breakup. Our findings suggest that migration movement may increase in response to climate change as ice concentration and access to prey declines, potentially increasing nutritional stress on bears.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".