Home Ranges and Spatial Organization of Fishers, <em>Martes pennanti</em>, in Central British Columbia
Bibliographic record
Abstract
We described the size and spatial arrangement of aggregate and seasonal home ranges for 17 radio-tagged resident Fishers (Martes pennanti) that were >1.5 years old in two areas of central British Columbia during 1990-1993 and 1996-2000. We estimated home range size for each Fisher from the 95% isopleth of the utilization distribution generated using a fixed kernel model with smoothing selected by least-squares cross-validation (95% FK). For comparison to previous studies, we also calculated the minimum convex polygon estimate of home range size (MCP) for each animal. The aggregate home ranges (95% FK) of female Fishers (mean = 37.9 km², SD = 18.5, range = 10.5 – 81.2, n = 11) were significantly smaller than those of males (mean = 161.3 km², SD = 100.0, range = 46.0 – 225.2, n = 3; P = 0.019). We observed minor overlap among 95% FK home ranges of Fishers of the same sex, but considerable overlap among home ranges of males and females. Home ranges (95% FK or MCP) that we observed in central British Columbia were larger than those reported elsewhere in North America, particularly for males. We suggest that the distribution of resources for Fishers may occur at lower gross densitiesin central British Columbia than in other portions of the Fisher’s range and that suitable habitat in which Fishers can establish home ranges is not found uniformly across the landscape.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".