Sea Ice Influences Habitat Type Use by Great Black-Backed Gulls (Larus marinus) in Coastal Newfoundland, Canada
Bibliographic record
Abstract
The influence of an unusual concentration of sea ice and breeding failure on the foraging movement patterns and habitat use of Great Black-backed Gulls (Larus marinus) was investigated. GPS loggers were deployed on three incubating females when multi-year sea ice moved into foraging ranges, dividing the tracking period (1–23 June 2017) into ice-free (5–10 days) and ice-present periods (11–12 days). Foraging trip parameters (e.g., distance, duration) differed among individuals but not with ice conditions. Great Black-backed Gulls decreased use of islands when ice was present (0.05 ± 0.08 locations/trip) relative to absent (5.9 ± 0.5 locations/trip), but increased use of marine habitat when ice was present (9.4 ± 0.2 locations/trip) relative to absent (2.9 ± 0.2 locations/trip). Great Black-backed Gulls also moved at higher speeds in areas of 91–100% ice cover relative to < 50% ice cover, suggesting that low percent cover sea ice acts as important at-sea foraging/roosting sites. Additionally, two Great Black-backed Gulls that were continuously tracked during post-breeding failure repeatedly visited the colony throughout July-August, suggesting some advantage to maintaining a presence at nest sites.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".