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Record W1986619648 · doi:10.1139/z03-088

Prey use by red foxes (<i>Vulpes vulpes</i>) in urban and rural areas of Illinois

2003· article· en· W1986619648 on OpenAlexvenueno aff
Shana R. Lavin, Timothy R. Van Deelen, Patrick W. Brown, Richard E. Warner, Stanley H. Ambrose

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsVulpesHabitatPredationEcologyTrophic levelBiologyCanisCompetition (biology)

Abstract

fetched live from OpenAlex

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 (δ 13 C and δ 15 N) 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 (δ 13 C and δ 15 N) than rural coyotes, indicating that coyotes caused foxes to consume prey items from higher trophic levels and eat more C 4 plants. Urban foxes had lower isotopic values (δ 13 C and δ 15 N) 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 C 3 plant 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.189
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2003
Admission routes1
Has abstractyes

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