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Record W2761539699 · doi:10.3390/ani7100078

Environmental Impact and Relative Invasiveness of Free-Roaming Domestic Carnivores—a North American Survey of Governmental Agencies

2017· article· en· W2761539699 on OpenAlexaboutno aff
Ana Lepe, Valerie Kaplan, Alirio Arreaza, Robert Szpanderfer, David R. Bristol, Michael B. Sinclair

Bibliographic record

VenueAnimals · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeGeographyRecreationHabitatHarmEnvironmental impact assessmentFelisEnvironmental protectionEcologySocioeconomicsFisheryBiologyCATSMedicinePolitical science

Abstract

fetched live from OpenAlex

A survey of the United States and Canadian governmental agencies investigated the environmental impact and relative invasiveness of free-roaming domestic non-native carnivores-dogs, cats, and ferrets. Agencies represented wildlife, fish, game, natural or environmental resources, parks and recreation, veterinary and human health, animal control, and agriculture. Respondents were asked to document the number and frequency of sightings of unconfined animals, evidence for environmental harm, and the resulting "degree of concern" in their respective jurisdictions. Results confirmed the existence of feral (breeding) cats and dogs, documenting high levels of concern regarding the impact of these animals on both continental and surrounding insular habitats. Except for occasional strays, no free-roaming or feral ferrets were reported; nor were there reports of ferrets impacting native wildlife, including ground-nesting birds, or sensitive species. This is the first study to report the relative impact of free-roaming domestic carnivores. Dogs and cats meet the current definition of "invasive" species, whereas ferrets do not. Differences in how each species impacts the North American environment highlights the complex interaction between non-native species and their environment. Public attitudes and perceptions regarding these species may be a factor in their control and agency management priorities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.990

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2017
Admission routes1
Has abstractyes

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