Urban gulls of the Pacific coast of North America: Balancing wildlife conflict concerns with the protection of urban breeding sites
Bibliographic record
Abstract
Abstract: Gulls are one of the very few avian taxa to occur on all continents, and many species have adapted remarkably well to living alongside humans in urban environments. Paradoxically, many of these species continue to experience overall population declines, even as urban-nesting populations grow. In this talk, we will present details about several gull species that occupy the Pacific coast of North America: Glaucous-winged (Larus glaucescens), Western (L. occidentalis), Herring (L. argentatus), Mew (L. canus), and Heermann's (L. heermanni). Drawing on six years of field work across Canada and the US, we will compare and contrast the urban ecologies of these species with those of their declining, non-urban conspecifics. Nest site demographics, as well as colony density, nest fidelity, and reproductive success figures will be presented. We will also discuss several spatial models that we have developed to predict urban-nesting behaviour in these species. As urban-nesting becomes more commonplace, a host of new human-wildlife conflicts can arise, as well as many exciting conservation opportunities. These issues and ideas demand a deeper knowledge of the breeding ecology of urban gulls, and our work represents another step toward this. Authors: Edward Kroc¹, Louise Blight² ¹University of British Columbia, ²Procellaria Research & Consulting
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".