The spatial scale of variability in small‐mammal populations
Bibliographic record
Abstract
We studied small‐mammal populations across a range of spatial scales to determine which scales were relevant to demographic variability. We predicted that a scale of variability in population structure would occur at the scale of dispersal, which has previously been described as < 200 m for some small‐mammal species. Systematic live‐trapping surveys were conducted on nested grids at three scales: 1) extent = 4900 ha. grain =1000 m; 2) extent = 306 ha, grain = 250 m: and 3) extent = 31 ha. grain = 125 m. Prior to the present study, small‐mammal populations had not been systematically live‐trapped across a similar range of scales. From 1996 to 1998, surveys were conducted on a landscape intensively managed for timber, and on a reference landscape. Spatial analysis of abundance data demonstrated that small‐mammal populations (four species) exhibited positive spatial autocorrelation at distances of between 133 and 533 m depending on the species and the landscape. No higher‐order population structure was detected. Thus, within the range of scales we sampled, variability in small‐mammal abundance occurred over short distances (i.e. 133–533 m).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".