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Record W3112467131 · doi:10.1071/wr20205

Estimating habitat characteristics associated with the abundance of free-roaming domestic cats across the annual cycle

2022· article· en· W3112467131 on OpenAlexaffabout
Hannah E. Clyde, D. Ryan Norris, Emily Lupton, Elizabeth A. Gow

Bibliographic record

VenueWildlife Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsNature Conservancy of CanadaUniversity of Guelph
Fundersnot available
KeywordsAbundance (ecology)HabitatGeographyContext (archaeology)EcologyTemperate climateEcosystemBiology

Abstract

fetched live from OpenAlex

Context Domestic cats (Felis catus) hold an important place in human society but can negatively impact ecosystems when roaming freely outdoors. Aims Specific research goals included identifying factors associated with cat abundance over the year. Methods We deployed trail cameras in Wellington County, Ontario, Canada to estimate what habitat characteristics were associated with cats in the spring/summer and the fall/winter. Within a subset of our study area, we also compared these findings to a previous study that used walking surveys. Key results In the spring/summer, cat abundance was positively related to proximity to buildings and negatively related to distance to agriculture. In the fall/winter, cat abundance was negatively related to the presence of coyotes (Canis latrans) and positively related to proximity to major roads. Overall, cat abundance was higher in urban than rural locations, and higher in spring/summer compared to fall/winter. Both our results from trail cameras and walking surveys from a previous study identified that median income, woodlots, and major roads were important habitat characteristics associated with cats during the summer, and we discuss the costs and benefits associated with both approaches. Conclusions Free-roaming cats are associated with different habitat characteristics in spring/summer versus fall/winter and vary in abundance across landscape type and season. Implications The development of management strategies aimed at reducing free-roaming cats in temperate areas should incorporate seasonal and landscape patterns.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.034
GPT teacher head0.404
Teacher spread0.369 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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