Socio-spatial organization of Eurasian badgers (<i>Meles meles</i>) in a low-density population of central Europe
Bibliographic record
Abstract
We studied the socio-spatial organization of Eurasian badgers (or European badgers), Meles meles (L., 1758), in a low-density population (estimate 1.8 badgers/km2) inhabiting a semi-rural area of western Switzerland. For this purpose, 8 badgers (5 males and 3 females) were caught at 5 different main setts and were radio-tracked between May 1994 and November 1996. The size of individual home ranges varied from 0.27 to 3.74 km2 (1.69 ± 1.33 km2 (mean ± SD), n = 8, 100% MCP), seemingly according to local variations in habitat productivity. Individual home ranges were spatially stable, but their size decreased significantly during winter (0.26 ± 0.42 km2, n = 7, 100% MCP). Badger social units consisted of 1–5 adults and (or) subadults (2.2 ± 1.5 animals, n = 9) and their yearly offspring. Group-range size varied from 0.57 to 3.74 km2 (2.12 ± 1.30 km2, n = 4) and seemed to be influenced by the spatial distribution pattern of food resources. Indeed, each group range encompassed approximately the same surface of agricultural land (about 0.60 km2). Territories were not well marked, some group ranges partly overlapped. Latrines, which were not numerous and principally located inside rather than along borders of group ranges, were only used irregularly or sporadically. This prompts us to encourage the reconsideration of the role of territorial behaviour in promoting group formation in Eurasian badgers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".