Nest-site selection in the Canada Warbler (<i>Wilsonia canadensis</i>) in central New Hampshire
Bibliographic record
Abstract
Reproductive success in birds is largely influenced by nest-site selection. Nest predators are the greatest source of nest failure for most species of birds. Species that nest on the ground may be particularly adapted to maximally conceal nests to reduce the risk of loss to predators. Little is known about nest-site selection in the Canada Warbler ( Wilsonia canadensis (L., 1766)), a small ground-nesting Neotropical migrant. We predicted higher amounts of vegetative cover at successful nests of Canada Warblers compared with unsuccessful nests because detection by predators would decrease with greater cover. We measured vegetative characteristics (concealment, stem densities, ground cover) around each nest and compared these variables between successful and unsuccessful nests and between actual nests and mock nest sites on and off territories. Greater concealment and higher stem densities were the main features surrounding a successful nest site. Nest sites had significantly greater concealment when compared with both random mock nest sites on and off territories. Thus, concealment is important for this ground nester and achieved primarily through thick cover and strategic nest placement in vertical substrate with an inconspicuous opening to the nest cup. Forests with complex ground structure and thickets of small-stemmed woody plants should be targets of conservation when considering how to manage this declining species.
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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 | 0.000 | 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".