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Record W1967500510 · doi:10.3168/jds.2008-1135

Herd- and Cow-Level Prevalence of Foot Lesions in Ontario Dairy Cattle

2008· article· en· W1967500510 on OpenAlexafffundabout
G. Cramer, K. Lissemore, C.L. Guard, K.E. Leslie, D.F. Kelton

Bibliographic record

VenueJournal of Dairy Science · 2008
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
FundersDairy Farmers of OntarioAmerican Association of Bovine Practitioners
KeywordsHoofHerdClawDairy cattleMedicineLamenessVeterinary medicineLesionAnimal scienceFoot (prosody)BiologySurgeryAnatomy

Abstract

fetched live from OpenAlex

The objectives of this cross-sectional study were to determine herd-level and cow-level prevalence estimates for 11 foot lesions in Ontario dairy cattle. Foot lesions were recorded by 5 hoof trimmers on 13,530 cows in 204 Ontario dairy herds from March 2004 to May 2005. Significant differences existed between free-stall and tie-stall housing. In free-stall housing systems, 46.4% of cows had a foot lesion, compared with 25.7% of cows in tie-stall barns. Digital dermatitis was the most common lesion in tie stalls, occurring in 9.3% of cows and 69.7% of the herds, whereas in free-stall herds, 22.7% of cows and 96.7% of the herds were affected. The most common hoof horn lesions were hemorrhages and ulcers, at 7.7 and 4.7% in tie-stall housing and 11.0 and 9.2% in free-stall housing, respectively. Foot blocks were used to treat 2.2% of cows in free stalls and 0.3% in tie stalls. Intraclass correlation coefficients ranged from 9.5 to 17.3 for hoof horn lesions and 28.0 to 38.7 for infectious lesions. In summary, foot lesions diagnosed at the time of hoof trimming are common in Ontario, and appropriate treatment for hoof horn lesions is low.

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)0.000
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 score0.303
Threshold uncertainty score0.610

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.139
GPT teacher head0.339
Teacher spread0.200 · 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

Citations153
Published2008
Admission routes3
Has abstractyes

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