Ruffed grouse brood habitat selection at multiple scales in Pennsylvania: implications for survival
Bibliographic record
Abstract
Declines in ruffed grouse ( Bonasa umbellus (L., 1766)) populations in the central and southern Appalachians may be linked to low brood survival. Therefore, managing for high-quality brood habitat could improve grouse numbers. Understanding how brood habitat selection affects survival and the spatial scale at which this occurs is therefore fundamental to developing effective habitat management strategies. From 1999–2002, we monitored 38 broods for 5 weeks post hatch and estimated utilization distributions (n = 28), site-scale habitat use (n = 21), and daily survival rate (mean = 0.966, range = 0.920–0.997, and n = 19). Relative to available habitat, broods selected sites with greater herbaceous ground cover and higher small (<2.5 cm diameter at breast height, DBH) stem densities and landscapes containing higher proportions of road and young deciduous forest. Herbaceous ground cover provided arthropod prey and concealment from predators and was a primary factor driving habitat selection. High stem densities and early successional habitats provided increased security, but were only used if adequate ground cover was present. Broods strongly selected roads and experienced higher survival near edges. However, higher road densities were associated with lower survival at the landscape scale. This pattern reflects the differential scale at which grouse and their predators respond to edge.
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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 | 0.000 |
| 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) | 0.001 | 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".