The impact of location, habitat, and climate on morphological variation in the Western Deermouse (<i>Peromyscus sonoriensis</i>: Rodentia)
Bibliographic record
Abstract
Abstract Peromyscus sonoriensis is a widespread species ranging from southern Texas to the Yukon, from the Mississippi to the Pacific. Because of this extensive range, there are substantial differences in morphology due to variation in temperature, precipitation, and habitat. We used 2 data sets (n = 4,840 and n = 20,175) to study morphological differences of their crania and appendages. Consistent with Allen’s rule (shorter appendages with colder temperature), both data sets show a strong, positive, correlation between tail length and the average January temperature. However, there was an equally strong, but negative, correlation between tail length and average July temperature. We observed similar results for feet and crania. Ear length had a significant negative correlation with July average temperature but no correlation with January average temperature. When we controlled for temperature, cranial and appendage length increased with latitude, which was opposite of what we expected. Furthermore, longitude had a strong impact as mice trapped further west had longer appendages. When divided into habitats, forest deer mice are more likely than prairie or desert deer mice to demonstrate morphological responses to differences in climate, location, and year trapped. Our results show that P. sonoriensis exhibit notable morphological variation linked to location, habitat, and climate.
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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 | 0.001 | 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".