Influence of field technique, density, and sex on home range and overlap of the southern red-backed vole (<i>Myodes</i> <i>gapperi</i>)
Bibliographic record
Abstract
Home range is shaped by an individual’s interactions with the environment and conspecifics, and both size and placement may vary in response to population fluctuations. The method used to collect locational data may also affect home-range estimates. We examined the effect of density, sex, and field method on home range of southern red-backed voles (Myodes gapperi (Vigors, 1830)) inhabiting eastern hemlock (Tsuga canadensis (L.) Carrière) forests. Twelve mark–recapture grids were used to census M. gapperi from 2014 to 2017. In 2017, individuals were radio-collared. Home-range size, core-area size, and shared space were calculated using kernel density estimators from both mark–recapture and radiotelemetry data. Density effects on home range and core area were analyzed and differences between sex and field method were compared. We found (i) density did not affect home-range size, (ii) male home range was larger than female home range, (iii) females shared space more frequently and to a greater extent with males than other females, and (iv) home-range estimates were not significantly different between mark–recapture and radiotelemetry. Male home range, however, was larger under radiotelemetry and may reflect a truncation effect when mark–recapture grid size is smaller than male home range.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".