Within‐population craniometric variability of insular populations of deer mice, <i>Peromyscus maniculatus</i> , elucidated by landscape configuration
Bibliographic record
Abstract
Within‐population genetic variability of twelve insular and four mainland populations of deer mice ( Peromyscus maniculatus ) was assessed using craniometric characters, and compared to results previously obtained from RAPD data. An index of Craniometric Variance ( CVar ) was computed from pairwise distances among all specimens. Variations in CVar measures were then compared to landscape variables using a linear regression approach. Our results suggest that CVar decreases in presence of large number of a competitive species (the boreal redback vole, Clethrionomys gapperi ; r =−0.527, p <0.037) in deer mouse populations. Island remoteness ( r =−0.251, p <0.220) and the geometry of the bank opposite to each island ( r =−0.459, p <0.067) were marginally correlated with CVar , but the linear combination of these two variables, forming a composite isolation index, represented the major factor explaining the observed CVar ( r =−0.648, p <0.011). Using a multiple regression model, 76.3% of the CVar was explained by a combination of this isolation index and the competitors’ abundance. These results suggest that taking into account landscape barriers as well as the dispersal behavior of small mammals might provide sounder ecological variables than geographical distances alone for predicting within‐population genetic variability in a network of habitat patches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".