Life on the edge: the demography of short‐season populations of deer mice
Bibliographic record
Abstract
We documented populations of deer mice in the Kananaskis Valley, southwestern Alberta, Canada from 1979 to 1997 to determine whether these short‐season populations were more, or less, variable than populations in more temperate environments. We then examined patterns of reproduction, age‐specific survival, and immigration to explain variation in summer population growth. Population densities showed no multi‐annual periodicity and were generally low. At maximum, numbers doubled over the breeding season, but declined over the breeding season in 4 of 16 yr. Variability in population density was low, and similar to that of Peromyscus populations in more temperate environments. No demographic parameters were related to spring population densities, and immigration rates were low when conditions for survival of nestlings and adults were favorable. Variation in summer population growth was attributed primarily to variation in nestling survival among years.
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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 | 0.000 |
| 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 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".