Factors affecting nest-site selection of Sabine's Gulls in the eastern Canadian Arctic
Bibliographic record
Abstract
The selection of breeding habitat is of prime importance for individual fitness. Among birds, natural selection should favour the ability to recognize and select habitat suitable for nesting and rearing chicks. This study compares the characteristics of Sabine's Gull, Xema sabini (Sabine, 1819), nest sites with random points across a coastal tundra environment on Southampton Island, Nunavut, Canada. The availability of terrestrial invertebrate prey was also examined among habitats. Sabine's Gull nests were nonrandomly distributed in relation to vegetation, substrate, and proximity to water. Gulls nested within approximately 1 km of the coastline and selected sites with the greatest proportions of moss and standing water (i.e., they nested close to the edge of small freshwater ponds near shore). However, there were no detectable differences in characteristics between successful and unsuccessful nests within preferred habitat. The dynamics of terrestrial invertebrate prey communities varied between years, but the volume of invertebrates in Sabine's Gull nesting habitat was intermediate between the most productive habitats and the least productive habitats in both years. However, nest-site selection in Sabine's Gulls may also be influenced by the availability of aquatic invertebrates (not examined in this study) and their proximity to the marine coastline, where chicks are taken to be reared.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".