SNAG USE BY FORAGING BLACK-BACKED WOODPECKERS (PICOIDES ARCTICUS) IN A RECENTLY BURNED EASTERN BOREAL FOREST
Bibliographic record
Abstract
We studied snag use for foraging by Black-backed Woodpeckers (Picoides arcticus) one year after a fire in an eastern black spruce (Picea mariana) boreal forest in Quebec, Canada. We searched for signs of foraging (bark flaking and excavation holes) by Black-backed Woodpeckers on 6,536 snags sampled in 56 plots located in portions of the burned forest that had not been salvage logged. A logistic regression model was developed based on the presence or absence of foraging signs. Results showed that Black-backed Woodpeckers used larger snags that were less deteriorated by fire (qualified as high-quality snags). Direct field observations of individuals foraging on 119 snags also indicated that used snags corresponded to those of high predicted quality. Finally, we assessed the relationship between food availability and snag characteristics by measuring the density of wood-boring beetle larvae holes on 30 snags of different size and deterioration classes. High-quality snags contained higher prey densities (wood-boring beetle holes) than smaller and more deteriorated snags. We recommend that forest blocks characterized by large and less deteriorated trees be preserved from salvage logging in recently burned boreal forests in northeastern North America.
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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".