The influence of climate variability on polar bear (<i>Ursus maritimus</i>) and ringed seal (<i>Pusa hispida</i>) population dynamics
Bibliographic record
Abstract
Unusually high polar bear (Ursus maritimus Phipps, 1774) predation on ringed seal (Pusa hispida (Schreber, 1775)) pups and increased survival of polar bear cubs during mild springs is documented in published articles. Strong predation on newborn ringed seal pups in early spring, however, is likely to lower the overall energy intake of polar bears if ringed seal pups are their main food, because the energetic value of ringed seal pups increases 7–8 times during the 6 week lactation period. So although hunting success in early spring increases cub survival during the period after den emergence,when they are most vulnerable, it is likely to increase the number of starving bears later in the season. This negative-feedback effect of strong spring predation will not occur in areas where other seal species are abundant during summer, and polar bears in such areas are likely to exhibit population growth during periods with milder springs, at least until the ringed seal population has been depleted. Long time series of population estimates that can be used to test this hypothesis do not exist, but it is strongly supported by catch statistics for polar bears and ringed seals from east Greenland.
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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 | 0.001 |
| 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) | 0.001 | 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".