Coyote survival in a row-crop agricultural landscape
Bibliographic record
Abstract
With intensive farming, planting and harvest are the primary disturbance factors driving cover dynamics that influence wildlife communities. A top predator, coyotes ( Canis latrans Say, 1823) impact other wildlife when populations are high. Thus, knowledge of coyote demographics in agricultural habitat is critical to understanding ecosystem dynamics. We studied survival of 59 radio-collared coyotes (28 juveniles, 31 adults) from 1996 to 2001 in intensively farmed central Illinois. Logistic regression suggested that age and year were important covariates, but sex was not. Divergence in age-specific Kaplan–Meier survival functions occurred during fall harvest because of higher mortality among juveniles. Annual survival (30 April – 29 April) was 0.59 (95% CI = 0.47–0.71) for adults and 0.13 (0.06–0.20) for juveniles captured after June 1. Shooting (58% of mortality) was the principal cause of mortality, followed by road kills (24%) and other mortalities. Mortality of juveniles following agricultural harvest probably occurs because of inexperience, dispersal through unfamiliar territory, intense human activity, and catastrophic loss of agricultural cover. In contrast, we recorded no shootings of coyotes during the growing season when agricultural cover was highest (14 June – 29 September) despite a year-round open hunting season on coyotes in Illinois.
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 | 0.000 | 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 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".