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Record W3084715031 · doi:10.1136/vr.105781

Reliability of a beef cattle locomotion scoring system for use in clinical practice

2020· article· en· W3084715031 on OpenAlexfundno aff
Jay Tunstall, Karin Mueller, Oscar Sinfield, Helen M. Higgins

Bibliographic record

VenueVeterinary Record · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
FundersAnimal Welfare FoundationAnimal Welfare Foundation of Canada
KeywordsLamenessScoring systemCLIPSReliability (semiconductor)Beef cattleKappaMedicineCategorizationPhysical therapyPhysical medicine and rehabilitationComputer scienceMathematicsSurgeryArtificial intelligenceAnimal scienceBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Locomotion (lameness) scoring has been used and studied in the dairy industry; however, to the authors' knowledge, there are no studies assessing the reliability of locomotion scoring systems when used with beef cattle. METHODS: A four-point scoring system was developed and beef cattle filmed walking on a firm surface. Eight veterinary researchers, eight clinicians and eight veterinary students were shown written descriptors of the scoring system and four video clips for training purposes, before being asked to score 40 video clips in a random order. Participants repeated this task 4 days later. RESULTS: The intra-observer agreement (the same person scoring on different days) was acceptable with weighted mean Kappa values of 0.84, 0.81 and 0.84 respectively for researchers, clinicians and students. The inter-observer agreement (different people scoring the same animal) was acceptable with weighted Gwet's Agreement Coefficient values of 0.70, 0.69 and 0.64 for researchers, clinicians and students. Most disagreement occurred over scores one (not lame but imperfect locomotion) and two (lame, but not severe). CONCLUSION: This scoring system has the potential to reliably score lameness in beef cattle and help facilitate lameness treatment and control; however, some disagreements will occur especially over scores one and two.

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)0.001
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 score0.101
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.236
GPT teacher head0.417
Teacher spread0.181 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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