Determinants of home range size and space use patterns in a protected wolf (<i>Canis lupus</i>) population in the central Apennines, Italy
Bibliographic record
Abstract
Large carnivores are amongst the most susceptible species to human activities, and human-modified environments pose a threat to carnivore conservation. Wolves (Canis lupus Linnaeus, 1758) in the central Apennines, Italy, have coexisted with humans since historic times and represent a good case study to assess their spatiotemporal response to anthropogenic factors. From 2008 to 2010, we investigated the spatial behavior of wolves (seven wolves in five packs and six floaters) in the Abruzzo Lazio and Molise National Park. Orographically corrected annual home ranges of resident wolf packs, estimated through the Brownian bridge movement model, averaged 104 ± 24 km2 (mean ± SD), whereas floaters used two- to fourfold larger areas (293.8–408.7 km2). We did not detect any seasonal effect on home range size, but home ranges were larger during the night and in areas of greater road density, especially during summer. By estimating core areas through an individual-based approach, we also revealed a habitat-mediated response to human presence and activity, as resident wolves preferentially established core areas at greater elevation and in the more forested and inaccessible portions of the home range.
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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".