The influence of habitat selection on Canada Goose Branta canadensis nest success on Akimiski Island, Nunavut, Canada
Bibliographic record
Abstract
Predation avoidance is likely the foremost factor driving nest site selection among ground-nesting birds. The consequences of nest site selection were investigated on nest success for Canada Geese Branta canadensis breeding on Akimiski Island, Nunavut, Canada in 2010. Habitat features were measured at the scale of a nesting territory, at the nest site ( n = 241), and at random locations for both scales. Compared with paired random locations, nests were more likely to be in woody vegetation located closer to water at the territory scale and had less lateral vegetative cover but taller vegetation nearer to the nest site. Geese did not select nesting locations in vegetation that provided maximum cover, but rather located nests in areas providing both concealment from predators and visibility for the nesting female to enable early predator detection. Assessing the effect of habitat attributes on nest success did not yield unambiguous results, with the most parsimonious model showing an increasing probability of nest success with increasing nest age alone. Although nest site selection was not random, we suggest that increasing parental investment and declining predation risk (not likely mutually exclusive) through the breeding season had more influence on Canada Goose nest success on Akimiski Island than did choice of specific habitat features or spacing of nests.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".