Predation of Harp Seals, <i>Pagophilus groenlandicus</i>, by Polar Bears, <i>Ursus maritimus</i>, in Svalbard
Bibliographic record
Abstract
Harp seals (Pagophilus groenlandicus) that breed in February and March in the White Sea migrate to open water around Svalbard and Franz Josef Land in the Barents Sea, feeding pelagically while following the receding ice edge northward to the edge of the polar pack. Although harp seals are present throughout the area during the summer, they are primarily pelagic and do not appear to be extensively preyed upon by polar bears (Ursus maritimus). However, occasionally, large numbers of harp seals may haul out and rest on the pack ice or feed in the water below the ice and surface to breathe between the floes. When approached by a polar bear while on the ice, harp seals do not exhibit the instant flight response characteristic of the polar bear’s primary prey species, ringed (Pusa hispida) and bearded seals (Erignathus barbatus). In this situation, polar bears may make multiple kills without either consuming their own prey or scavenging seals killed by other bears. This behavior appears not to frighten other nearby harp seals, whether hauled out on the ice or in the water below the floes. These unusual concentrations of harp seals hauled out on sea ice may be related to the distribution and abundance of fish or other epontic prey. Their lack of an escape response to predators on the surface of the sea ice is probably a result of briefly hauling out in large numbers in spring while whelping on the sea ice in areas where the consequences of potential polar bear predation are insignificant. The rare events of harp seal mortality from bears killing them on the surface of pack ice during the summer do not appear to have a significant impact at the population level of either species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".