Prevalence of foot lesions in Québec dairy herds from 2015 to 2018
Bibliographic record
Abstract
Our first objective was to estimate the prevalence of foot lesions by type of milking system in dairy cows examined during regular hoof-trimming sessions between 2015 and 2018 in Québec dairy herds. A secondary objective was to describe the effect of day-to-day variation, cow, and herd characteristics on the prevalence of foot lesions. Data included 52,427 observations (on a cow during a specific trimming session) performed on 28,470 cows (≥2 yr old) from 355 herds. Only observations from trimming sessions in which ≥90% of the lactating herd was trimmed were considered. Lesions were recorded at the hoof level by 17 trained hoof trimmers between March 23, 2015, and July 10, 2018, using a computerized recording system. Hoof-level information was then matched with cow information and centralized at the Eastern Canada Dairy Herd Improvement. Foot lesions were classified into 6 categories: infectious, white line disease, heel erosion, ulcers, hemorrhages, and any type of foot lesions. Prevalence of each outcome was quantified using the marginal predicted mean probability estimated from a null generalized linear mixed model with a logit link, and accounted for clustering of observations by cow and by herd. Variance was partitioned to assess the variation in the probability of the outcomes attributable to each level of the data structure (day of exam, cow, and herd). Prevalence of a given foot lesion as function of milking system and of various explanatory variables (mean herd size, herd average daily production, breed of the cow, age of the cow at trimming, and year of the visit) was then estimated using a generalized linear mixed model. At least 1 foot lesion was observed in 29% of cows examined during regular trimming sessions in Québec from 2015 to 2018. Prevalence for any type of lesion was 27% for pipeline, 38% for robotic milking, and 41% for milking parlors. The highest prevalence of infectious lesions (mainly digital dermatitis) was observed in milking parlors and robotic systems, while the most prevalent lesions in pipeline were hemorrhages. Herd-level factors explained most of the disease probability for infectious diseases, heel erosion, and hemorrhages. Therefore, control of these diseases should be based on applying best herd-management practices. On the other hand, probabilities of white line disease and sole ulcers were mainly determined by cow-level characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".