Can old-growth forests managed by irregular shelterwood treatments maintain habitats suitable for an indicator species of old boreal forests?
Bibliographic record
Abstract
Irregular shelterwood treatment (IST) is a silvicultural method involving successive partial harvests to promote regeneration under forest canopy, particularly suited for maintaining irregular stand structures and, under certain conditions, conserving old-growth forest attributes. However, its effectiveness remains poorly documented. The Black-backed Woodpecker ( Picoides arcticus (Swainson)), a species associated with old-growth forests and early decay dead wood, was selected as a focal species to study old-growth boreal forest managed with IST. We assessed habitat selection at landscape and home range scales in managed and unmanaged forests. At the home range scale, we evaluated foraging and nest site selection. Selection was determined using Manly's selection ratios and generalized linear models with a logit link. At the landscape scale, the probability of establishing a home range increased with the proportion of IST, and individuals selected IST in proportion to its availability. At the home range scale, the probability of selecting a foraging site increased with snag diameter and woody debris volume, while only the diameter of snags predicted the probability of selecting a nest site. These attributes show no significant difference between IST and old-growth forest classes. Black-backed Woodpeckers use IST, transition, and true old-growth forests in proportion to their availability. Our findings suggest recent IST can provide suitable breeding habitat for Black-backed Woodpecker, particularly when large-diameter snags are retained.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".