Influences of weather and moonlight on activity patterns of small mammals: a biogeographical perspective
Bibliographic record
Abstract
We analyzed 15 years of trapping data on prairie voles (Microtus ochrogaster) and cotton rats (Sigmodon hispidus) to elucidate behavioural responses to weather by season and time of day. Use of such a long-term data set is rare and ameliorates many of the problems with short-term data sets typically used for such analysis. The trapping was conducted in the east-central part of Kansas (U.S.A.), near the southern edge of the distribution of prairie voles and the northern edge of the distribution of cotton rats. These distributions provide the framework for differing hypotheses as to responsiveness of individuals of the two species to weather phenomena as indicated by the probability of capture. Probability of capture was statistically significantly affected by weather, most frequently by precipitation and temperature. Effects varied with season and between species, and were generally consistent with hypotheses based on the northern (boreal and temperate) history of prairie voles and southern (subtropical and temperate) history of cotton rats and with predation-avoidance hypotheses. Variation in the probabilities of capture of cotton rats was more associated with weather, especially in the colder seasons, than was variation in the probabilities of capture of prairie voles. In summer, capture rates of prairie voles were more susceptible to weather than were those of cotton rats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".