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Record W4376891003 · doi:10.3390/ani13101647

Internet-Based Survey on Physical Activity and Incidence of Injury in Active Working Dogs

2023· article· en· W4376891003 on OpenAlexaboutno aff
Giuseppe Spinella, Simona Valentini, Mirella Lopedote

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)Hip dysplasiaWildlifePhysical examinationPhysical therapySurgeryRadiography

Abstract

fetched live from OpenAlex

A survey with 100 multiple choice and open-ended questions was proposed by free access to working dogs' handlers. One hundred and nine respondents were recorded and their dates processed. The most represented breeds were: Belgian Malinois, Labrador, Border Collie and German Shepherds. Of these, 71.6% were intact dogs and 28.4% were spayed or neutered, with a median age range of 3-4 years. Furthermore, 55.5% had undergone early radiographic examinations for hip or elbow dysplasia diagnosis. The dogs performed the following activities: search and rescue on surface (59%), search and rescue on rubble (37%), Internationale Gebrauchshund Pruefung (IGP) (9%), man trailing (5%), sled dog (5%), search on avalanche (4%), dog towing (3%), canine shows (3%), hunting (2%), water rescue (1%), pet therapy (1%), wildlife conservation dog (1%), Mondioring (1%). Only 36.4% of respondents submitted their dogs to a specific sports medical examination and 55.5% to an orthopaedic examination. An injury incidence of 45.5% was recorded, generally related to mild musculoskeletal trauma. A limited number of handlers routinely performed warm-up and/or cool-down activities. A positive assessment emerged of the need for many respondents to attend and request education courses and updates on the proper health management of their 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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.459

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 head0.128
GPT teacher head0.375
Teacher spread0.247 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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