Relationships Between Energy Balance and Health Traits of Dairy Cattle in Early Lactation
Bibliographic record
Abstract
The objective of the study was to calculate phenotypic relationships between energy balance in early lactation and health and reproduction in that lactation. Data were 26,701 daily records of dry matter intake and milk production, periodic measures of milk composition and body weight, and all health and reproductive information from 140 multiparous Holstein cows. Daily energy balance was calculated by multiplying feed intake by the concentration of energy of the ration and subtracting the amount of energy required for maintenance (based on parity and body weight) and for milk production (based on yield and concentrations of fat, protein, and lactose). Six measures of energy balance were defined: mean daily energy balance during the first 20, 50, and 100 d of lactation; minimum daily energy balance; days in negative energy balance; and total energy deficit. Measures of health were the numbers of occurrences of each of the following during lactation: all udder problems, mastitis, all locomotive problems, laminitis, digestive problems, and reproductive problems. Reproductive traits were the number of days to first observed estrus and number of inseminations. Several significant relationships between energy balance and health were observed. Increased digestive and locomotive problems were associated with longer and more extreme periods of negative energy balance.
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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 | 0.001 | 0.000 |
| 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".