Landscape use by fishers (<i>Pekania pennanti</i>): core areas differ in habitat than the entire home range
Bibliographic record
Abstract
Home ranges have long been studied in animal ecology. Core areas may be used at a greater proportion than the rest of the home range, implying the core contains dependable resources. The Pacific fisher (Pekania pennanti (Erxleben, 1777)) is a rare mesocarnivore occupying a small area in the Sierra Nevada Mountains, California, USA. Once statewide, fishers declined in the 1900s due to trapping, habitat fragmentation, and development. Recently, drought induced by climate change may be affecting this population. We examined space use of fishers in their core versus their home range for levels of anthropogenic modifications (housing density, road density, silvicultural treatments), habitat types, and tree mortality. We found core areas contained more late-successional forest and minimal human activity compared with their territory. Their core had higher levels of dense canopy and higher amounts of conifer cover, while minimizing the amount of buildings, developed habitat, and low canopy cover. Fishers may in effect be seeking refugia by minimizing their exposure to these elements in their core. Conserving landscape components used by fishers in their core areas will be important for the persistence of this isolated population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".