MétaCan
Menu
Back to cohort
Record W3005473029 · doi:10.3390/ani10020258

Uncontrolled Outdoor Access for Cats: An Assessment of Risks and Benefits

2020· review· en· W3005473029 on OpenAlexaff
Sarah Ming Li Tan, Anastasia C. Stellato, Lee Niel

Bibliographic record

VenueAnimals · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of GuelphUniversity of British Columbia
Fundersnot available
KeywordsCATSWildlifeBusinessWelfareAnimal welfareEnvironmental healthMedicineEcologyBiologyEconomics

Abstract

fetched live from OpenAlex

Uncontrolled outdoor access is associated with a number of welfare concerns for companion cats, including increased risks of disease and parasites, injury or death due to traffic, predation or ingestion of toxic substances, and getting permanently separated from their owner. In addition, cats pose a threat to local wildlife due to predatory behaviors, and can sometimes be a nuisance to human neighbors. Despite these concerns, recent estimates suggest that many owners are still providing their cats with uncontrolled outdoor access, likely because it also offers welfare benefits by allowing cats to perform natural behaviors, such as hunting, exploring, and climbing. While some have suggested that outdoor access is necessary to meet cats' behavioral needs and to prevent related behavioral problems, others have recommended various environmental enrichment strategies that can be developed to meet these needs within an indoor environment or through supervised and controlled outdoor access. This review examines the welfare issues and benefits associated with outdoor access for cats, as well as what is currently known about peoples' practices, knowledge, and attitudes about the provision of outdoor access for cats.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.211
GPT teacher head0.540
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations90
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnimalsSame topicHuman-Animal Interaction StudiesFrench-language works237,207