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Record W4385270582 · doi:10.1371/journal.pone.0288081

Dog breeds and conformations in the UK in 2019: VetCompass canine demography and some consequent welfare implications

2023· article· en· W4385270582 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersGoddard Space Flight CenterKennel Club Charitable TrustDogs TrustAgria Djurförsäkring
KeywordsPurebredBreedCrossbreedVeterinary medicineDemographyAnimal welfareLabrador RetrieverMedicineAnimal healthBiologyAnimal scienceSurgeryEcology

Abstract

fetched live from OpenAlex

INTRODUCTION: Growing concerns over health and welfare impacts from extreme phenotypes in dogs have created an urgent need for reliable demographic information on the national breed structures of dogs. METHODS: This study included all dogs under primary veterinary care in the UK during 2019 at practices participating in VetCompass. Demographic data on these dogs were analysed to report on the frequency of common breeds and also to report on conformation, bodyweight, sex and neuter associations with these breeds. RESULTS: The study included 2,237,105 dogs under UK veterinary care in 2019. Overall, 69.4% (n = 1,551,462) were classified as purebred, 6.7% (149,308) as designer-crossbred and 24.0% (536,335) as nondesigner-crossbred. Across 800 unique breed names, the most frequent breeds at any age were nondesigner-crossbred (n = 536,335, 24.0%), Labrador Retriever (154,222, 6.9%) and Jack Russell Terrier (101,294, 4.5%). Among 229,624 (10.3%) dogs aged under one year, the most frequent breeds were nondesigner-crossbred (n = 45,995, 20.0%), French Bulldog (16,036, 7.0%) and Cockapoo (14,321, 6.2%). Overall, based on breed characteristics, 17.6% (395,739) were classified as brachycephalic, 43.1% (969,403) as mesaticephalic and 8.3% (186,320) as dolichocephalic. Of 1,551,336 dogs that were classifiable based on breed, 52.6% (815,673) were chondrodystrophic. Of 1,462,925 dogs that were classifiable, there were 54.6% (n = 798,426) short haired, 32.6% (476,883) medium haired and 12.8% (186,934) long haired. Of 1,547,653 dogs that were classifiable for ear carriage, 24.5% (n = 379,581) were erect, 28.1% (434,273) were semi-erect, 19.7% (305,475) were v-shaped drop and 27.7% (428,324) were pendulous. Overall, there was a 1.09:1.00 ratio of male (n = 1,163,512; 52.2%) to female dogs (n = 1,067,552; 47.8%). CONCLUSIONS: Health and welfare issues linked to popular breeds with extreme phenotypes suggest that there is much work to do to help owners to make more welfare-friendly decisions when choosing which type of dog to own.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.227

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)0.0000.000

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 head0.042
GPT teacher head0.315
Teacher spread0.272 · 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