Habitat Patch Use by Fishers in the Deciduous Forest-Dominated Landscape of the Central Appalachian Mountains, USA
Bibliographic record
Abstract
Abstract Fishers (Pekania pennanti) are often associated with the coniferous and mixed forests of the northern United States and central Canada, and their ecology has been studied extensively in portions of their distributional range. Recently, natural range expansion and reintroductions have led to recolonization by fishers to portions of the central Appalachian Mountains, where deciduous forest is the dominant vegetation type. We used noninvasive hair-snare surveys and microsatellite genetic analysis to detect fishers in the central Appalachian Mountains of Pennsylvania. We used these detections within an occupancy modeling framework to explore habitat patch use by fishers and the forest characteristics and land use features that influenced it. We found that the likelihood of patch use by fishers was related to forests with higher proportions of low-density residential areas. Our results also suggested that lower road densities might be related to higher likelihood of fisher patch use. Fishers in Pennsylvania tolerated some forms of land development. Patch use was not driven by forest type or canopy cover, at least within our deciduous forest-dominated study areas. Future research identifying threshold values at which forest cover and land development affect patch use by fishers in the central Appalachian Mountains will better inform management decisions with respect to sites for future reintroduction of fishers.
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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 | 0.000 | 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".