Phenotypic study of body condition scores in Canadian dairy cattle
Bibliographic record
Abstract
The objective of this study was to characterize body condition score (BCS) in dairy cattle recorded under commercial conditions in Quebec. There were 354 958 BCS records from Holstein cows and 15 334 records from Ayrshire cows available, from first to fifth parity, recorded by producers using a scale of 1 to 5. A classification model was fitted including fixed effects of herd, year of calving, month of calving, age at calving, and days in milk (DIM); another model substituted the Wilmink function on days in milk to model lactational BCS curves. Both models used the spatial power covariance structure to account for correlation between BCS recorded on the same cow along the lactation and were fitted using the Mixed procedure of SAS software (version 9.1.3). Body condition score was significantly affected by month of calving, age at calving and DIM. Average BCS (SD) for Holstein cows from first to fifth lactation were: 2.95 (0.46), 2.90 (0.53), 2.93 (0.56), 2.94 (0.57), and 2.93 (0.57), respectively; the estimates for Ayrshire cows from first to fifth lactation were: 2.99 (0.47), 3.06 (0.54), 3.15 (0.56), 3.11 (0.57), 3.13 (0.58). Lactation curves for BCS were generated for each breed and parity. The routine collection of BCS in individual cows is recommended to ensure close monitoring of energy balance during the lactation. The BCS information analyzed in this study is suitable for modeling changes throughout the lactation with the application of the Wilmink function. Key words: Body condition score, Canadian dairy cattle
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".