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Record W2615921165 · doi:10.11575/prism/10185

Human-coyote (Canis latrans) interaction in Canadian urban parks and green space: Preliminary findings from a media-content analysis

2008· article· en· W2615921165 on OpenAlexaboutno aff
Shelley M. Alexander, Michael S. Quinn

Bibliographic record

VenueOpen MIND · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsCanisCarnivoreHuman–wildlife conflictWildlifeGeographyHabituationEcologyHuman animalEcosystemLivestockPsychologyPredationBiologyForestry

Abstract

fetched live from OpenAlex

The coyote (Canis latrans) is a highly adaptable apex carnivore that provides a critical ecological function in urban ecosystems.Habituation of coyotes results in behavioural changes which can lead to human-wildlife conflict.Understanding human awareness, values and attitudes towards coyotes, and the potential for human-coyote conflict, is essential to managing for effective ecological function of urban protected areas.A highly charged debate over coyotes and urban park management often plays out in the media, especially after a public report of a negative encounter.We conducted a content analysis of 215 primary articles from the print media (1995 -2008) that focused on coyotes in urban parks and green space.We identified the types (i.e., coyote versus human or pet) and frequency of interactions, we summarized wound descriptions for pets versus humans, and we compared the type and frequency of incidents by human demographic.We also detailed the relative positive versus negative content of articles, the common descriptors of coyotes, the dominant concerns or effects arising from the reported conflict, and the various management responses to the interaction.The paper presents preliminary results of the analysis within a human-wildlife conflict framework and provides recommendations for urban park management.

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 score0.178
Threshold uncertainty score0.994

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)0.0070.000

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 head0.043
GPT teacher head0.258
Teacher spread0.215 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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