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Record W7052238554

The relationship between humans and urban foxes on Prince Edward Island

2015· article· en· W7052238554 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2015
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeFeelingPopulationDingoUrban ecologyWildlife management
DOInot available

Abstract

fetched live from OpenAlex

This study examines the relationship between humans and urban foxes living in Charlottetown, Prince Edward Island. In recent years colloquial evidence suggests that there has been an increase in foxes living in urban areas of Prince Edward Island, which may eventually lead to an increase in negative interactions. An online questionnaire was administered to 456 residents of Prince Edward Island, in order to explore their attitudes and interactions towards foxes. The survey results were examined using thematic analysis in order to identify common attitudes towards foxes in residents of Charlottetown. The themes identified were: positive attitudes towards urban foxes, negative attitudes towards urban foxes, concern for the safety of foxes, concern for the safety of humans, concern about loss of wildlife habitat, and concerns about humans feeding foxes. Our findings showed that most respondents on Prince Edward Island have positive attitudes and feelings towards foxes. In order to explore the relationship between humans and urban foxes, the histories of foxes in England and Prince Edward Island were documented and compared. Foxes began entering urban areas in England during the 1940s, and were initially received well. Due to a large population of bold urban foxes, as well as outbreaks of disease in the fox population, foxes are now less welcome in urban areas of England. By comparing human attitudes regarding foxes between Prince Edward Island and England, we hope to obtain insights into future attitudes towards foxes on Prince Edward Island.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

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