Nest-site characteristics and breeding success of three species of boreal songbirds in western Newfoundland, Canada
Bibliographic record
Abstract
Delineating habitat requirements and preferences of species is essential for conservation planning. We studied nest habitat use and effects of microsite vegetation characteristics on breeding success of yellow-rumped warblers ( Dendroica coronata (L., 1766)), blackpoll warblers ( Dendroica striata (J.R. Forster, 1772)), and white-throated sparrows ( Zonotrichia albicollis (Gmelin, 1789)) in an area with a low extent (<6% of available land) of forest harvest in northwestern Newfoundland. During 2004 and 2005, 99 nests were located and monitored, and the characteristics of nest sites measured. Vegetation at yellow-rumped and blackpoll warbler nest sites differed from random sites; however, within used sites, no vegetation characteristics were significantly associated with success. White-throated sparrow nest sites contained more downed wood and less ground vegetation than did random sites; however, successful nests were associated with different variables than those that distinguished them from random sites, including less canopy cover and less woody debris. Thus, whereas yellow-rumped and blackpoll warblers used specific nest-site characteristics and white-throated sparrows had higher nest success associated with certain characteristics, the nest characteristics these birds appeared to choose did not have demonstrable fitness benefits.
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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 | 0.001 |
| 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".