Movement patterns, habitat selection, and corridor use of a typical woodland-dweller species, the European pine marten (Martes martes), in fragmented landscape
Bibliographic record
Abstract
Woodland fragmentation through land consolidation practices (the merging of small fields by the removal of separating structures like hedgerows) is recognised as a major threat to biodiversity in Europe. While its impact on the occurrence of species has frequently been the object of focus, its impact on the movements of individuals has rarely been studied. We used paths of radio-tracked European pine martens ( Martes martes (L., 1758)), a forest-dwelling species, with fixes taken at 3 min intervals to determine their habitat use in fragmented landscape. Our results differ from those generally reported in the literature. Monitored individuals were not confined to large forests, and made additional use of small wood plots and hedgerows. Indeed, individuals moved faster in forests than in all other habitat types, which suggests that martens preferentially foraged in small woods, edges, and hedgerows. Roads and buildings were not avoided; fields, however, were avoided, although they did not act as barriers. Martens stayed close to forest cover when venturing into open ground, which suggests that although not restricted to forests, pine martens exhibit a certain dependence on the presence of trees in the vicinity.
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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".