Prevalence of elevated milk β-hydroxybutyrate concentrations in Holstein cows measured by Fourier-transform infrared analysis in Dairy Herd Improvement milk samples and association with milk yield and components
Bibliographic record
Abstract
The purpose was to describe the prevalence and effect of elevated milk β-hydroxybutyrate (BHB) as detected by routine Fourier-transform infrared analysis in Dairy Herd Improvement milk samples. Data collected over 4 yr included cow information as well as milk yield and composition from 498,310 samples from postparturient Holstein cows (5-35d in milk) from 4,242 herds. The following thresholds were used to classify cows based on their early lactation milk BHB concentration: <0.15mmol/L=negative; 0.15 to 0.19mmol/L=suspect; and ≥0.20mmol/L=positive. Overall prevalence (suspect + positive) was 22.6% and was higher for older cows (18.7, 19.5, and 27.6%, for cows in their first, second, and third or greater lactation, respectively). Distribution with regards to days in milk was different among parity groups, with first-lactation cows having highest prevalence (30%) in the first week after calving; cows in their second and third and greater parity had the highest prevalence in the second week after calving, at 25.8 and 34.6%, respectively. Season of calving affected the prevalence of elevated milk BHB, with cows calving in the fall and spring seasons showing higher prevalence. Distribution among herds was highly variable, as 45% of herds had a prevalence of 20% or less, 47% of herds had a prevalence between 21 and 40%, 6% of herds had a prevalence between 40 and 50%, and 2% of herds had a prevalence of 50% or above. Positive cows had lower milk yield, protein concentration and yield, and lower Transition Cow Index than negative cows, but also higher fat concentration and yield, as well as higher somatic cell count than negative cows. Suspect cows were generally intermediate. The present analysis highlights the opportunity for elevated milk BHB monitoring at the herd level through routine BHB testing in Dairy Herd Improvement milk samples.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".