Space use of polar bears (<i>Ursus maritimus</i>) in Davis Strait in relation to sea ice and harp seals (<i>Pagophilus groenlandicus</i>)
Bibliographic record
Abstract
Polar bears ( Ursus maritimus Phipps, 1774) rely on seals as their primary prey, yet predator–prey spatial relationships are poorly understood. We examined the spatial relationship between Davis Strait polar bears and harp seals ( Pagophilus groenlandicus Erxleben, 1777), using satellite telemetry for both species. We analyzed sea ice trends using remote sensing (1979–2021) to examine how their environment may be changing using four sea ice seasons (freeze-up, winter, break-up, and summer). Sea ice cover decreased and summer season lengthened over time. Polar bears ( n = 18) tracked in 1991–2001 for 7–12 months had a mean 95% minimum convex polygon (MCP) home range size of 108 146 km 2 (standard error of the mean (SE) = 18 252 km 2 ) and a mean 95% kernel density home range size (kernel density estimate (KDE)) of 76 863 km 2 (SE = 12 260 km 2 ). Harp seals ( n = 22) tracked for 5–8 months in 1993–2005 had a mean 95% MCP of 693 403 km 2 (SE = 74 384 km 2 ) and a mean 95% KDE of 395 316 km 2 (SE = 48 688 km 2 ). During freeze-up, the core-use areas of both species did not overlap, but the broad-use areas did. During break-up, the broad-use areas overlapped more than the core-use areas. The space use of both species was influenced by the sea ice seasons and these seasons have changed over time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".