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Record W3212417020 · doi:10.1002/vetr.1080

Survey investigating the reasons why UK‐based gundogs ceased working between 2010 and 2019

2021· article· en· W3212417020 on OpenAlexaboutno aff
J. E. F. Houlton

Bibliographic record

VenueVeterinary Record · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLongevityMedicineWorking populationPopulationDemographySignificant differenceLamenessGerontologyPediatricsSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: To determine the longevity of the working life of gundogs in the UK, whether owners' considered retirement was premature and to identify risk factors associated with such work. METHODS: A web-based survey seeking owner information as to the longevity of their dogs' working life and why they were retired, euthanised or died. The sample population is six hundred sixty-five dogs. RESULTS: The median age at which Springer spaniels stopped work was 11 years and for Cocker spaniels, it was 9 years. The median age for Labrador retrievers was 10 years; for Golden retrievers, 11 years and Flat-coated retrievers, 9.5 years. Cocker spaniels stopped work at a significantly younger age than Springer spaniels (p = 0.0003) or Labrador retrievers (p = 0.0407). There was no significant difference between the other major breeds. The majority of owners (54.3%) were satisfied with the working lifespan of their dog. Seventy per cent of dogs were retired, the three most prevalent reasons being lameness (25.2%), old age (23.7%) and deafness (7.8%). Forty-four dogs died (6.6%) and 158 (24%) were euthanised, with cancer (58%) being the most common reason. CONCLUSIONS: No work-related issues were identified and gundogs appear to have similar causes of mortality to the general canine population.

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 score0.064
Threshold uncertainty score0.127

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.110
GPT teacher head0.354
Teacher spread0.244 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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