Local- and landscape-level den selection of striped skunks on the Canadian prairies
Bibliographic record
Abstract
We examined the seasonal landscape and habitat use patterns of striped skunks ( Mephitis mephitis Schreber, 1776). We tracked 52 male and 72 female skunks from September 1999 to June 2003 in Saskatchewan, Canada. At the local level, den structures differed by sex and season. In autumn/winter, all skunks preferred buildings, whereas in spring/summer females selectively used underground burrows and rock piles for parturition and rearing of young, and males used aboveground retreats. Den sites were closer to crop fields, roads, water sources, and macrohabitat edges than random sites. At the landscape level, den sites were associated positively with weighted mean shape index of crop fields, mean patch size of water bodies, total edge of water bodies, and weighted mean fractal dimensions of grassland, woodland, and farmsteads, suggesting that wetland edges and habitat complexity are important in den selection by skunks. Compositional analysis revealed sex- and season-specific differences at the population level. Both sexes preferred grassland/pastures and farmstead habitats for establishing den sites. In autumn/winter, skunks preferred grassland/pastures for winter dens. However, in spring/summer skunks preferred farmsteads for resting sites. Our results suggest that skunks respond to landscape and habitat features surrounding den sites, and not just specific den or local habitat characteristics.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".