Habitat selection by nesting and brood-rearing sharp-tailed grouse
Bibliographic record
Abstract
Breeding success is a critical component of population stability and is often influenced by the habitats used during the breeding season. Current hypotheses suggest that sharp-tailed grouse ( Tympanuchus phasianellus (L., 1758)) select nest and brood-rearing habitats that provide both lateral and overhead cover to avoid detection by predators. We examined the selection of nesting and brood-rearing habitats of sharp-tailed grouse at three spatial scales (landscape, patch, and site) in northeastern British Columbia using standard and conditional logistic regression models and an information–theoretic approach. At the patch and site scales, our results validate our predictions, as nesting females selected shrub–steppe habitats, greater shrub and grass cover, taller vegetation, and greater residual vegetation compared with random sites. Brood-rearing females selected for agricultural habitats during the early brood-rearing period (0–14 days), but did not show selection of any habitat type or site attribute during the late brood-rearing period (15–49 days). We suggest that the selection of shrub-dominated habitats by nesting females supports the hypothesis that females select sites and habitats that maximize concealment. We further suggest that selection of shrub-dominated habitat is occurring in response to changes in habitat conditions and availability, as natural grassland communities have diminished across the landscape.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".