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Record W3094606432 · doi:10.7120/09627286.29.4.399

Evaluating factors influencing dog post-adoptive return in a Canadian animal shelter

2020· article· en· W3094606432 on OpenAlexaffabout
JR Friend, C.J. Bench

Bibliographic record

VenueAnimal Welfare · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAggressionAnimal welfareAnimal-assisted therapyHUBzeroCanisDemographyPsychologyAnxietyRisk factorPopulationMedicineEnvironmental healthPet therapyDevelopmental psychologyPsychiatryBiologyInternal medicineEcology

Abstract

fetched live from OpenAlex

Abstract Understanding the factors associated with post-adoptive return in dogs (Canis familiaris) is important for reducing shelter return rates. The objective of this retrospective study was to identify factors detectable in shelters associated with post-adoptive return in an objective dog-centric analysis. The records of 959 dogs were evaluated via factor analysis of seven behaviour and seven physical variables which resulted in the extraction of six principal factors. Fear aggression, ongoing health concerns, separation anxiety, sex-specific aggression, and age effect on source were not found to significantly impact outcome. In particular, dog aggression risk (a factor composed of breed, size, and dog aggression) was found to be significantly higher in returned dogs. Since dog aggression risk is associated with post-adoptive return, this could help shelters to modify policies to either screen aggressive dogs from the adoption population or improve adoption counselling in an attempt to help lower return rates.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.357
Threshold uncertainty score0.719

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.360
Teacher spread0.320 · 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 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

Citations13
Published2020
Admission routes2
Has abstractyes

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