Data from: Study "American Herring Gulls - GPS - Lobster Bay, Southwest Nova Scotia, Canada"
Bibliographic record
Abstract
Anthropogenic food subsidies attract opportunistic generalists like gulls in high densities, which may lead to negative impacts on human communities and local ecosystems. Managing impacts requires understanding why gulls use particular natural or industrial sites at different times of day or phases of the breeding cycle. Use of natural and human-influenced habitats likely varies temporally as gulls alter schedules and site selection to match the predictability of different resources as they vary through space and time relative to patterns in human activities and natural rhythms, whilst gull resource requirements and restrictions to movement also shift with changing reproductive demands. We quantified seasonal and circadian patterns in American herring gull interactions with anthropogenic and natural sites throughout breeding using GPS data from 15 gulls tracked over three years. We examined the weekly probability of gull occurrence at distinct destinations (e.g., islands, offshore, fish processing plants), and how occurrence varied with time of day, weekday/weekend, and tide phase, using GLMMs with a binomial response for destination-specific occurrence. Probability at the colony varied predictably through the breeding season (highest attendance from dusk to dawn, during incubation and early chick rearing), providing confidence in the modelling approach for detecting temporal patterns in behaviour. Gulls visited other islands mostly outside incubation and chick rearing, from dusk through night, likely roosting. Occurrence offshore where interaction with fishing vessels is possible was highest from dusk to dawn, but was the most likely destination during incubation and early chick rearing. Occurrence at fish plants gradually increased until after fledging when attendance was highest from Aug-Oct coincident with the peak of Atlantic herring processing, and was more likely during the weekdays, during working hours, and during low and flood tide. Gulls in southwest Nova Scotia, Canada, have the behavioural flexibility to adapt to natural rhythms and human schedules when beneficial, enabling them to thrive in a region where industry and natural resources are abundant. These findings can provide information to guide when and where to test different subsidy management strategies locally, while also considering potential increased pressures on island ecosystems.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".