Food habits of European badgers (<i>Meles meles</i>) along an altitudinal gradient of Mediterranean environments: a field test of the earthworm specialization hypothesis
Bibliographic record
Abstract
Food specialization by European badgers (Meles meles) is a largely debated controversy. Data from Mediterranean areas indicate small importance of earthworms (Lumbricus spp.) in badger diet and support the idea that badgers are generalist predators. Nevertheless, only dry areas have been sampled so far. We studied badger diet in six areas along an elevation gradient with different rainfall and habitat conditions, which influenced earthworm availability. We evaluated the influence of earthworm availability on badger diet along this environmental gradient. Badgers used a wide range of prey items in the different habitats and seasons sampled. In contrast with other Mediterranean studies, earthworms made an important contribution to badger diet (27% of estimated volume). Earthworm occurrence in the diet was high in elevated and wet habitats and in spring and autumn–winter. Earthworm consumption was nonlinearly related to availability, indicating high intake compared with availability in wet areas. Moreover, in summer, availability was virtually zero in all habitats, whereas consumption averaged 15% volume of the diet. We tentatively suggest that badgers compensate for variations in earthworm availability by changing their foraging tactics. This suggests that badgers could be viewed as specialist foragers for earthworms in some Mediterranean environments.
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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 | 0.000 |
| 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".