Winter nutritional condition of eastern coyotes in relation to prey density
Bibliographic record
Abstract
In northeastern North America, coyotes (Canis latrans) contend with lower prey diversity and abundance relative to their western counterparts (Harrison 1992; Parker 1995; Patterson et al. 1998). We used urinalysis to determine if the local distribution and abundance of white-tailed deer (Odocoileus virginianus) and snowshoe hare (Lepus americanus) had a measurable effect on the nutritional condition of eastern coyotes during winter. We analyzed 567 urine specimens collected from coyotes belonging to 8 territorial family groups, whose territories contained different densities of deer and hare. Mean urinary urea nitrogen (UN) : creatinine (C) ratios were correlated positively with relative hare density (rs = 0.75, P = 0.004) but negatively with deer density (rs = -0.71, P = 0.009). Coyote-group size did not have a significant influence on mean UN:C (rs = 0.42, P = 0.17). Coyotes utilizing hare as a primary food source maintained consistently high UN:C values throughout the winter, whereas those using proportionally more deer as a primary food source exhibited lower and more variable UN:C values during the breeding season. Winter densities of deer and hare were inversely related (rs = -0.63, P = 0.027), further suggesting that the UN:C value was primarily a function of hare density. The analysis of urine voided in snow is useful for determining the relative time since last feeding for carnivores. However, inferring relative nutritional condition from time since last feeding may be inappropriate for cases in which carnivores exploit prey of different sizes.
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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 | 0.001 | 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".