The quantitative effects of population density and winter weather on the body condition of white-tailed deer (<i>Odocoileus virginianus</i>) in Nova Scotia, Canada
Bibliographic record
Abstract
Understanding the underlying mechanisms that cause variation in survival and the reproductive success of animals is essential for predicting variation in population parameters. To gain an understanding of the effects of density and winter weather severity on white-tailed deer, Odocoileus virginianus (Zimmermann, 1780), we examined the effects of current-year deer density and cumulative weekly average values for snow depth, rainfall, and the number of degrees below –15 °C until the time of death, as well as cumulative effects of density and snow depth over the previous one and two winters, on the body condition of adult females, adult males, and fawns. Model selection using Akaike's Information Criterion and multi-model inference suggested that snow depth was the best predictor of body condition for all three age/sex groups. Winter rainfall was the next most influential predictor for adult females and adult males but was not important in determining fawn body condition. Temperature had the least influence on the body condition of all three age/sex groups. Deer density during the winter of death had minimal effects for all groups and we found no evidence that cumulative multiyear variables influenced body condition. We hypothesize that cohort variation may better explain previous findings showing effects of multiyear variables. A model for estimating the proportion of animals in poor body condition for each age/sex group is presented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".