Factors affecting wild boar (<i>Sus scrofa</i>) occurrence in highly fragmented Mediterranean landscapes
Bibliographic record
Abstract
This paper is an analysis of the effects of forest fragmentation on wild boar (Sus scrofa) occurrence in coarse-grained fragmented landscapes (<20% forestscrubland cover on a landscape scale; N = 140 forest fragments, four regions) in central Spain. Occurrence was examined in relation to forest size, isolation, habitat quality, and region. Wild boar occurrence was mainly explained by the location of the forest fragments on the northern or southern plateau. Wild boars were more abundant on the northern plateau than on the southern plateau. In addition, wild boars are more frequent in large forest fragments adjacent to other large forests near mountains or riparian woodlands. The percent presence of wild boars in fragments varied among the four regions sampled (regional effect). Although wild boars occurred more frequently in large than in small forests, this pattern was less pronounced than that found in badgers (Meles meles), roe deer (Capreolus capreolus), and stone martens (Martes foina) and similar to that found in red foxes (Vulpes vulpes). The spatial distribution of wild boars may be affected by forest fragmentation despite their typical generalist life-history traits and potential use of agricultural areas as food habitats. These results support the idea that landscape pattern (degree of fragmentation and grain pattern) may be a determinant of species' abundance and distribution in fragmented landscapes.
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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 | 0.001 |
| 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".