The Feeding Ecology and Behavior of Breeding Iceland Gulls <i>Larus glaucoides kumlieni</i> and Comparisons with Sympatric Large <i>Larus</i> Gulls on Southwestern Baffin Island, Canada
Bibliographic record
Abstract
The feeding ecology and behavior of breeding Iceland Gulls Larus glaucoides kumlieni and, to a lesser extent, American Herring Gulls Larus smithsonianus and Glaucous Gulls Larus hyperboreus, were studied at a large Iceland Gull colony located near Kinngait, Baffin Island, Canada. Iceland Gulls collected food close to the colony, mainly at ebbing and low tides, on or very close to the shoreline, and in adjacent nearshore waters. The most common feeding technique was picking on the water surface while swimming. Additional techniques included plunging to capture food items at greater depths and kleptoparasitism. Known food items included marine invertebrates and small fish. American Herring and Glaucous gulls nested in the study area in substantially lower numbers than Iceland Gulls but used similar feeding habitats and techniques and collected similar food items. The specific feeding techniques used by all three species were typical of many large Larus gulls. Sufficient food availability at the time of the study may explain some of the observed similarities in feeding habitat use and behavior. An important component of broad niche separation for Iceland Gulls in areas of sympatry may be sea-cliff nesting and concentrated use of coastal marine habitats for feeding, including shorelines and nearshore areas. In addition, other studies have shown that American Herring and Glaucous gulls use more inland and terrestrial habitats, use more diverse foods, and employ different feeding behaviors, including more predation and scavenging. Additional studies in selected areas are needed to fully address the questions raised in this study.
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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 | 0.000 |
| 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 | 0.000 |
| 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".