Risk factors for digital dermatitis in free‐stall‐housed, Canadian dairy cattle
Bibliographic record
Abstract
BACKGROUND: A comprehensive analysis of the relation between digital dermatitis (DD) and cow and herd characteristics in Canadian dairies is currently lacking. METHODS: A multilevel logistic regression analysis was performed using 12,260 cow records from 62 dairy farms to assess association between 27 cow and herd-level variables, and presence of DD. RESULTS: The odds for a cow to have at least 1 DD lesion were higher in first-parity cows and those in later lactation (≥45 days in milk). Housing cows on a concrete base was associated with higher odds (OR 2.24) for DD when bedding was added once a week or less. Bedding the concrete base more frequently reduced odds for DD. Wood shavings or other bedding types were more positively associated with DD (OR 2.31 and 1.87, respectively) compared to sawdust. Also, the odds of DD were lower on farms with a scraping manure frequency of every 2 h compared to less frequent scraping (OR 0.54). CONCLUSION: Nine risk factors for DD were identified and quantified, with stall base, bedding type, and manure scraping frequency associated with lower odds of DD. DD prevalence could be reduced by implementing management practices for first-parity cows, as they had higher odds of DD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".