Foraging ecology of black-backed woodpeckers (<i>Picoides arcticus</i>) in unburned eastern boreal forest stands
Bibliographic record
Abstract
Managed coniferous forest dominates much of the black-backed woodpecker’s ( Picoides arcticus Swainson) breeding range. Despite this, little is known about the fine-scale foraging behaviour of this focal species in unburned managed forest stands in the absence of insect outbreaks. To investigate the foraging substrates used in such a habitat, we employed radio-telemetry to track a total of 27 black-backed woodpeckers. During two successive summers (2005–2006), 279 foraging observations were recorded, most of which were on dying trees, snags, and downed woody debris. Individuals frequently foraged by excavation, suggesting that in the absence of insect outbreaks the black-backed woodpecker forages mainly by drilling. The majority of foraging events occurred on recently dead snags with a mean dbh (±SE) of 18.3 ± 0.4 cm. Our results suggest that in unburned boreal forest stands, substrate diameter and decay class are important predictors of suitable foraging substrates for black-backed woodpeckers. We suggest that conservation efforts aimed at maintaining this dead-wood dependent cavity nesting species within the landscape, should endeavour to maintain 100 ha patches of old-growth coniferous forest. This would ensure the continuous production of a sufficient quantity of recently dead or dying trees to meet the foraging needs of this 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 | 0.000 | 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) | 0.000 | 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".