Delayed capelin (<i>Mallotus villosus</i>) availability influences predatory behaviour of large gulls on black-legged kittiwakes (<i>Rissa tridactyla</i>), causing a reduction in kittiwake breeding success
Bibliographic record
Abstract
During 1998 and 1999, the impact of predation by herring gulls (Larus argentatus) and great black-backed gulls (Larus marinus) on breeding success of black-legged kittiwakes (Rissa tridactyla) at Gull Island, Witless Bay, southeastern Newfoundland, was quantified in relation to the timing of the annual arrival of capelin (Mallotus villosus) to spawn. The frequency of predation attempts by large gulls on kittiwakes was compared among three periods: before the mean hatching date for herring gulls, between the mean hatching date for herring gulls and the arrival of the capelin, and following capelin arrival. The frequency varied significantly among the three periods, being highest after gull chicks hatched but before the capelin arrived. The frequency of gull predation was significantly correlated with the percentage of kittiwake eggs and chicks that disappeared each week. We estimated that 43 and 30% of kittiwake eggs and chicks at Gull Island were taken by gulls in 1998 and 1999, respectively. Kittiwakes have been indirectly (through increased predation by gulls) affected by the delayed arrival and lower abundance of capelin in recent years, which underlines the need to understand multispecies interactions when interpreting the effects of human alteration of the marine environment.
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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 | 0.000 | 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".