Prey use by red foxes (<i>Vulpes vulpes</i>) in urban and rural areas of Illinois
Bibliographic record
Abstract
Decline of red fox (Vulpes vulpes) populations in Illinois has been attributed to altered geographic landscapes and the eastward expansion of the coyote. To investigate effects of habitat use and competition with coyotes on diets of foxes in intensively farmed landscapes of Illinois, we analyzed carbon and nitrogen isotope ratios (δ13C and δ15N) of foxes, coyotes (Canis latrans), and other local species. Foxes were categorized as rural (agricultural habitat, coyotes present), urban (urban habitat, coyotes absent), or from an agricultural research facility at the University of Illinois (South Farms, agricultural habitat, coyotes absent). Rural foxes had higher fur isotopic values (δ13C and δ15N) than rural coyotes, indicating that coyotes caused foxes to consume prey items from higher trophic levels and eat more C4plants. Urban foxes had lower isotopic values (δ13C and δ15N) than South Farms foxes, suggesting that habitat use partly determined fox diets; foxes in urbanized habitats consumed prey at lower trophic levels within a largely C3plant based food web. Models of competitive exclusion by coyotes were better predictors of fox long-term diets, including pup rearing, while habitat use models predicted fox diets on a narrower timescale. Competitive exclusion by coyotes might be an important factor explaining the decline of foxes in the intense farming areas of Illinois.
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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 | 0.000 |
| 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 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".