Vigilance Decreases with Time at Loafing Sites in Gulls (<i><scp>L</scp>arus</i> spp.)
Bibliographic record
Abstract
Abstract Understanding how animals partition effort between vigilance for predators and other conflicting activities has been a mainstay of animal behaviour research. Classical theories implicitly assume that animals alternate between high and low vigilance states over short timescales, but that average effort invested in vigilance is constant over an extended bout of such alternations. However, one recent model suggests that vigilance should be adjusted dynamically to short‐term changes in the perception of predation risk and would tend to decrease with time. Indeed, as time passes by without disturbances, perception of the need for vigilance should decrease and prey animals may allocate more time to competing activities. Here, we examined how the proportion of sleeping gulls (Larus spp.) in loafing groups changed over time. Sleeping gulls can only maintain low levels of vigilance against external threats (compared to alert individuals), and we predicted that the proportion of sleeping gulls at loafing sites should increase over time when no disturbances occur. Statistically significant changes in the proportion of sleeping gulls as a function of time occurred in the majority of sequences and an increase was observed significantly more often than predicted by chance alone. This temporal pattern cannot be caused by reduction in hunger levels because gulls are not feeding at loafing sites. The results indicate that vigilance can be adjusted dynamically in response to short‐term temporal changes in the perception of predation risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".