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Record W2623401162 · doi:10.3168/jds.2016-12171

Technical note: Assessing lameness in tie-stalls using live stall lameness scoring

2017· article· en· W2623401162 on OpenAlexafffund
S. Palacio, L. Peignier, C. Pachoud, C.G.R. Nash, Steve Adam, Renée Bergeron, D. Pellerin, A.M. de Passillé, J. Rushen, Derek B. Haley, T.J. DeVries, E. Vasseur

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversité LavalUniversity of British ColumbiaUniversity of GuelphValacta (Canada)McGill UniversitySte. Anne's Hospital
FundersCanadian Dairy CommissionDairy Farmers of CanadaAgriculture and Agri-Food CanadaMcGill University
KeywordsLamenessHerdStall (fluid mechanics)MedicineTrue positive rateScoring systemVeterinary medicineSurgeryComputer science

Abstract

fetched live from OpenAlex

Video stall lameness scoring (SLS) has been shown to be comparable to video locomotion scoring for evaluating lameness in dairy cows housed in tie-stalls and may be a more practical and easier method to measure lameness in a herd. We compared live SLS to video SLS and to live locomotion scoring. A total of 685 lactating cows subsampled from 27 commercial dairy herds were examined for lameness through live and video SLS. Cows scored with the live or video SLS system were scored for 4 behavioral indicators while still in their stall: weight shifting (shift), standing on the edge of the stall (edge), uneven weight bearing while standing (rest), and uneven weight bearing while moving from side to side (uneven). Two observers live scored and video scored for SLS. Lameness prevalence from video SLS and live SLS were similar (31 vs. 30%, respectively). Prevalence of the behavioral indicators varied from 0.59 to 58.2%. Sensitivity and specificity of live SLS was calculated using video SLS as the gold standard for lameness detection in tie-stalls. Sensitivity of live SLS was 0.83 and specificity was 0.94. False positives and false negatives for lameness were 14.4 and 16.8%, respectively. When comparing the prevalence of lameness measured through video or live SLS at the herd level, live SLS for lameness was correlated to video SLS (r = 0.91) with a Cohen's kappa coefficient of 0.79 (95% confidence limit = 0.73-0.84). Average exact agreement in the behavior indicators observed ranged from 80 to 100%. A subsample of 250 cows from 5 herds were scored for live SLS and live locomotion by a third observer. Intra- and interobserver reliability for live SLS and live locomotion scoring were found to have a kappa coefficient of 0.53 (95% confidence limit = 0.43-0.64) when determining a cow as lame through SLS or locomotion scoring. Live SLS was correlated with live locomotion scoring (r = 0.92). However, lameness prevalence was lower when using live SLS (28.4%) compared with locomotion scoring (38%). In summary, live SLS may be an acceptable method to replace video SLS to identify lame cows and rank tie-stall herds in terms of lameness prevalence without having to remove the cows from their stalls or view videos to score; however, it may underestimate lameness prevalence compared with locomotion scoring methods.

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)0.002
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 score0.824
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
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 head0.135
GPT teacher head0.430
Teacher spread0.294 · 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

Citations26
Published2017
Admission routes2
Has abstractyes

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