Beating the odds: effects of weather on a short-season population of deer mice
Bibliographic record
Abstract
We examined 11 years of data on reproductive success, survival, and population dynamics of two populations (Fortress and Grizzly) of deer mice (Peromyscus maniculatus) in the Kananaskis Valley, Alberta, to investigate the extent to which the dynamics of these populations is dictated by weather conditions. Summer population growth was not related to the population growth in the winter preceding the breeding season or to spring population density. Over the summer on the Fortress grid, population growth was positively related to adult survival, whereas on the Grizzly grid, population growth was positively related to nestling survival. Neither summer population growth nor demographic correlates of summer population growth was consistently related to weather patterns. On Fortress, adult survival during the breeding season was negatively correlated with precipitation. On Grizzly, nestling survival during the breeding season was negatively correlated with precipitation. Winter population growth was inversely proportional to the fall population density prior to the winter but neither was related to weather conditions. Climate limits seasonal breeding in these populations, but compensatory responses appear sufficient to accommodate extreme weather conditions during both the breeding and nonbreeding seasons.
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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 | 0.001 |
| 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".