Living on the edge of a shrinking habitat: the ivory gull, <i>Pagophila eburnea</i> , an endangered sea-ice specialist
Bibliographic record
Abstract
The ongoing decline of sea ice threatens many Arctic taxa, including the ivory gull. Understanding how ice-edges and ice concentrations influence the distribution of the endangered ivory gulls is a prerequisite to the implementation of adequate conservation strategies. From 2007 to 2013, we used satellite transmitters to monitor the movements of 104 ivory gulls originating from Canada, Greenland, Svalbard-Norway and Russia. Although half of the positions were within 41 km of the ice-edge (75% within 100 km), approximately 80% were on relatively highly concentrated sea ice. Ivory gulls used more concentrated sea ice in summer, when close to their high-Arctic breeding ground, than in winter. The best model to explain the distance of the birds from the ice-edge included the ice concentration within approximately 10 km, the month and the distance to the colony. Given the strong links between ivory gull, ice-edge and ice concentration, its conservation status is unlikely to improve in the current context of sea-ice decline which, in turn, will allow anthropogenic activities to develop in regions that are particularly important for the species.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".