Predation by Bears on Woodpecker Nests: Are Nestling Begging and Habitat Choice Risky Business?
Bibliographic record
Abstract
We evaluated hypotheses explaining risk of predation by American Black Bear (Ursus americanus) at 418 Yellow-bellied Sapsucker (Sphyrapicus varius) and Hairy Woodpecker (Picoides villosus) nests, on the basis of nestling begging and nest-site habitat features in Algonquin Provincial Park, Ontario. Ninety-three percent of Yellow-bellied Sapsuckers in stands dominated by Sugar Maple (Acer saccharum) nested in Sugar Maple or American Beech (Fagus grandifolia) trees that were dead or in declining health, whereas 86% of Yellow-bellied Sapsuckers in stands dominated by aspen (Populus spp.) nested in Quaking Aspen (P. tremuloides) that were in declining health. Black Bears depredated 17% of 315 nests of Yellow-bellied Sapsuckers in Sugar Maple stands, which accounts for 71% of all Yellow-bellied Sapsucker nest failures. Only 1 (2%) of 46 Hairy Woodpecker nests in the same Sugar Maple stands was depredated by a bear. None of 51 Yellow-bellied Sapsucker nests in aspen stands was depredated. In Sugar Maple stands, daily nest survival of Yellow-bellied Sapsucker nests was lowest when nestling begging calls were loudest and carried the farthest, in more recently harvested stands, and in trees other than American Beech (mostly Sugar Maple). Nest substrates were hardest at Hairy Woodpecker nests, followed by successful Yellow-bellied Sapsucker nests in American Beech and Quaking Aspen; Yellow-bellied Sapsucker nests were softest in stands that had been harvested within the past 30 years. Our study suggests that the risk of predation by American Black Bears at woodpecker nests is a combined function of nestling begging calls, which attract bears to the nest, and nest habitat characteristics, which influence accessibility to the interior of the cavity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".