Long-Distance Movement of a Female Polar Bear from Canada to Russia
Bibliographic record
Abstract
Polar bears (Ursus maritimus) display fidelity to large geographic regions, and their movements are influenced by sea ice distribution. Polar bear subpopulations are moderately distinct from one another, and long-distance movements between subpopulations are rare. We describe and analyze the movements of a female polar bear tracked by satellite telemetry from spring 2009 for 798 days. This female traveled an exceptionally long distance (totaling 11 686 km) from the sea ice off the Yukon Territory, Canada (Southern Beaufort Sea subpopulation) to Wrangel Island, Russia (Chukchi Sea subpopulation). In comparison to other polar bears in this study, this bear traveled farther, moved faster, and had a much larger home range in the first year. Furthermore, the calculation of the home range size by two different methods demonstrated that the commonly used minimum convex polygon method overestimated the home range compared to the less biased Brownian bridge movement model. This female’s long-distance movement was unusual and provides additional evidence for gene flow between subpopulations. Monitoring polar bear movements is useful to track such events, which is especially important at present because sea ice loss due to climate change can affect subpopulation boundaries and influence management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".