Spatial organization of Molina’s hog-nosed skunk (Conepatus chinga) in two landscapes of the Pampas grassland of Argentina
Bibliographic record
Abstract
We radio-tracked 16 individuals (6 males, 10 females) of the little known Molina’s hog-nosed skunk ( Conepatus chinga (Molina, 1782)) and compared home-range dynamics, movement rates, and densities between a protected area and a landscape fragmented by agriculture. The mean home-range size (95% fixed kernel) was 166.7 ha (SD = 107.5 ha), without significant differences between areas. Home-range size varied significantly between males (mean = 243.7 ha, SD = 76.5 ha) and females (mean = 120.4 ha, SD =77.6 ha). Overlap between home range and core area was extensive between and within sexes in the protected area and more limited in the cropland area. Mean distance traveled between two consecutive resting sites was 269.5 m (SD = 365 m) and did not differ between areas, although movements were greater for males than females. Distance moved was influenced by seasons, being greater during the cold period. Finally, density estimates were consistently greater at the protected area. We argue that home-range size in Molina’s hog-nosed skunks is an inherent species property, whereas population density and territoriality are more flexible parameters that could reflect how the ecosystem state was affected. In our study, the greater dispersion of food patches in the cropland area than in the protected area may be the major factor influencing these parameters.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".