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 km2 (standard error of the mean (SE) = 18 252 km2) and a mean 95% kernel density home range size (kernel density estimate (KDE)) of 76 863 km2 (SE = 12 260 km2). Harp seals ( n = 22) tracked for 5–8 months in 1993–2005 had a mean 95% MCP of 693 403 km2 (SE = 74 384 km2) and a mean 95% KDE of 395 316 km2 (SE = 48 688 km2). 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".