Spatial structure and land-cover use in a low-density Mediterranean population of Eurasian badgers
Bibliographic record
Abstract
Eurasian badgers, Meles meles (L., 1758), have an extensive geographic range throughout which their social organization varies. Their capacity for intraspecific variation can now best be understood by studying them in landscapes that differ from the lush, lowland farmland where their tendency to form large groups has been most intensively investigated. Badgers in cork oak (Quercus suber L.) woodland are thus a priority for study, as this Mediterranean landscape provides an extreme contrast to those studied elsewhere. In this habitat in Portugal, we found 0.36–0.48 badgers/km2, one of the lowest population densities recorded in Western Europe. Here, individuals used seasonally stable home ranges that averaged 4.46 km2and that were occupied by 3–4 adults plus 3–4 cubs of the year. In this landscape, badgers selectively used cork oak woodland with understory and riparian vegetation. As predicted by the resource dispersion hypothesis, home-range size was positively correlated with food-patch dispersion. In southwestern Portugal, badgers depend upon an environmental mosaic such as olive groves and orchards and vegetable gardens for food and cork oak woodlands for shelter and protection.
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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".