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Record W2029603314 · doi:10.1136/vr.b4855

Incidence and impact of dog attacks on guide dogs in the UK

2010· article· en· W2029603314 on OpenAlexaboutno aff
Annabelle Brooks, Rachel Moxon, Gary England

Bibliographic record

VenueVeterinary Record · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
Fundersnot available
KeywordsBreedTrainerVeterinary medicineMedicinePopulationCrossbreedDog biteIncidence (geometry)Animal welfareLabrador RetrieverDemographyEnvironmental healthAnimal scienceSurgeryBiologyPathology

Abstract

fetched live from OpenAlex

In a retrospective survey, researchers identified 100 incidents of attacks on guide dogs by other dogs. These were reviewed in order to determine the number, severity and impact on the handler and dog, and the characteristics of the aggressors and victims. During the study period there were more than three attacks reported each month, with 61 per cent of the attacks being upon dogs that were in harness and working with an owner or trainer. The majority of the dogs that were attacked were male (62 per cent), and the breeds that were over-represented (relative to their prevalence in the general guide dog population) were the labrador and the golden retriever x flat-coated retriever crossbreed. Most of the attacks occurred in public places between 09.00 and 15.00 and the majority (61 per cent) of the attacking dogs were off the lead at the time of the attack. Thirty-eight per cent of the attacking dogs were of bull breeds, which were over-represented among attackers compared with the proportion of this breed type in the general dog population. Veterinary attention was sought after 41 per cent of the attacks, and in 19 per cent of instances there was injury to the handler or to a member of the public. The attacks were reported to have affected the working performance and behaviour of the victim dog in 45 per cent of the instances, and two dogs had to be subsequently withdrawn from working as guide dogs.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.331
Teacher spread0.307 · 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.

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

Citations25
Published2010
Admission routes1
Has abstractyes

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