Contrasting effects of the extent of sea‐ice on the breeding performance of an Antarctic top predator, the Snow Petrel <i>Pagodroma nivea</i>
Bibliographic record
Abstract
Recent studies have shown that the Antarctic Circumpolar Wave and the related sea‐ice cover around the Antarctic continent may have a profound effect on the lower trophic levels of the marine environment. In particular, extensive sea‐ice cover enhances the survival of krill. However, the effects of sea‐ice cover on top predators remain poorly understood. Using time series from 1973 to 1999, we examine the influence of regional sea‐ice extent on a number of indices of breeding performance of an avian predator, the Snow Petrel, in Antarctica. The percentage of breeding pairs was highly variable and there were fewer birds breeding when sea‐ice cover was extensive during July. By contrast, overall breeding success and fledgling body condition were improved during years with extensive sea‐ice cover during the preceding November and July–September. These results indicate that the same sea‐ice conditions may have different effects on the breeding performance of a species. The overall increase in winter sea‐ice extent during the last decade appears to have resulted in an overall improvement of the quality of fledglings produced, and thus probably of future recruitment.
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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".