Effects of sea ice fragmentation on polar bear migratory movement in Hudson Bay
Bibliographic record
Abstract
Habitat fragmentation can impede an animal’s ability to move through their habitat, affecting both local and long-distance movements. Each year, polar bears Ursus maritimus migrate to refuge habitats on land or to multiyear ice as annual sea ice breaks up. We used polar bear telemetry location data from 39 adult female polar bears tracked in Hudson Bay in 2013-2018 during break-up (2 May-23 July) to show variation in migratory movement and timing as break-up advances. We separated break-up into early and late periods and used standard deviation in temporal spatial autocorrelation (SASD) of sea ice concentration to quantify sea ice fragmentation. Higher spatial autocorrelation reflects dissimilarity in local habitat composition at a single point in time, while SASD reflects variation in local habitat composition over time. In late break-up, there was a significant positive correlation between polar bear path tortuosity and SASD. Individuals arrived on land significantly later when paths moved through sea ice with increasing SASD in late break-up. Reproductive status of adult female polar bears had no effect on the variability of the sea ice an individual travelled through. SASD provides a means of summarizing the complexity and dynamics of sea ice habitat and can be used to understand variation in movement and ecology of ice-associated organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".