Winter selection of roost sites by ruffed grouse during daytime in mixed nordic-temperate forests, Quebec, Canada
Bibliographic record
Abstract
We determined the categories of roost sites used by ruffed grouse ( Bonasa umbellus (L., 1766)) during daytime in winter from 245 radiotelemetric locations of 26 adult females. We conducted our study in the Réserve faunique de Portneuf, located in a mixed nordic-temperate softwood–hardwood forest in Quebec, Canada. We evaluated the effects of weather, snow, and habitat variables on the incidence of snow burrowing, tree roosting, and on-snow roosting using mixed multinomial models, ANOVA, and logistic regressions. The best logistic regression model of snow burrowing probability was identified using the Akaike path. The incidence of each category of roost sites was 41.2% tree roosts, 36.3% snow burrows, and 22.4% on-snow roosts. Coniferous canopy closure and depth of fluffy snow were the variables that influenced roosting behavior the most. Probability of snow burrowing increased with compaction depth and decreased with coniferous cover. Probability of tree roosting increased with temperature. On-snow roosts had a denser lateral obstruction than snow burrows, whereas tree roosts had a greater coniferous basal area, stem density, and canopy cover than snow burrows. Stand type also influenced the incidence of each category of roost sites, snow burrows dominating in deciduous stands and tree roosts dominating in mixed and coniferous stands.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".