A tale of two polar bear populations: ice habitat, harvest, and body condition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract One of the primary mechanisms by which sea ice loss is expected to affect polar bears is via reduced body condition and growth resulting from reduced access to prey. To date, negative effects of sea ice loss have been documented for two of 19 recognized populations. Effects of sea ice loss on other polar bear populations that differ in harvest rate, population density, and/or feeding ecology have been assumed, but empirical support, especially quantitative data on population size, demography, and/or body condition spanning two or more decades, have been lacking. We examined trends in body condition metrics of captured bears and relationships with summertime ice concentration between 1977 and 2010 for the Baffin Bay (BB) and Davis Strait (DS) polar bear populations. Polar bears in these regions occupy areas with annual sea ice that has decreased markedly starting in the 1990s. Despite differences in harvest rate, population density, sea ice concentration, and prey base, polar bears in both populations exhibited positive relationships between body condition and summertime sea ice cover during the recent period of sea ice decline. Furthermore, females and cubs exhibited relationships with sea ice that were not apparent during the earlier period (1977–1990s) when sea ice loss did not occur. We suggest that declining body condition in BB may be a result of recent declines in sea ice habitat. In DS, high population density and/or sea ice loss, may be responsible for the declines in body condition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.002 | 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 it