Bear presence attracts avian predators but does not impact lesser snow goose daily nest attendance
Bibliographic record
Abstract
The presence of foraging bears in Arctic breeding bird colonies has been increasingly reported in the literature, and these may constitute disturbance events which cause incubating birds to leave their nest. Avian predators may associate with bears during such events, likely to capitalize on unattended nests in the presence of bears. Here, we estimated changes in daily nest attendance of lesser snow geese Anser caerulescens caerulescens in the presence of foraging bears, and estimated the association between foraging bears and avian predators. We predicted decreased nest attendance by geese on days with bears, and close associations between avian predators and bears. We monitored snow goose nests with cameras from 2013 to 2018 to assess nest attendance behaviours on days when bears were in the colony compared to control days without bears. When bears were present in colonies, we estimated the probability of avian predator occurrence compared to control periods. When controlling for day of incubation and camera placement types, we found no significant effects of bears on daily nest attendance behaviours of snow geese (n = 85). We found a significantly higher probability of observing avian predators when bears were present (0.72) compared to control periods without bears (0.11). We show that snow geese do not alter daily nest attendance in the presence of foraging bears, and suggest this is due to the presence of avian predators.
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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".