Age and condition of deer killed by coyotes in Nova Scotia
Bibliographic record
Abstract
Coyote (Canis latrans) predation is a major source of mortality for white-tailed deer (Odocoileus virginianus) in many areas of northeastern North America. However, if coyotes primarily remove deer that would have died of other causes in the absence of predation (compensatory mortality), the impact of predation would be minimal regardless of the number of deer removed. We examined the carcasses of 102 white-tailed deer consumed by coyotes during winter in southwestern Nova Scotia (Queens County) and on Cape Breton Island from 1992 to 1997. Sixty-nine deer were victims of predation, five died of other natural causes, two were killed in coyote snares, two were killed on the road, two were shot and not recovered during the autumn hunting season, and one was shot and abandoned in early winter. The causes of death of the remaining 21 deer could not be determined. Fawns were overrepresented in the sample of coyote-killed deer on Cape Breton Island, but the age distribution of deer killed by coyotes in Queens County did not differ significantly from that of local road-killed deer. Femur marrow fat reserves of deer killed by coyotes appeared to be as good as or better than those of road-killed deer in the vicinity of each study area. During winter, coyotes often killed deer in situations where deer were disadvantaged either by deep snow or by poor footing on frozen lakes. This may help explain the general lack of selection of weaker animals. Our data are consistent with the idea that mortality due to coyote predation was largely additive to mortality due to other factors. However, manipulative experiments are needed to verify this conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".