The association of serum metabolites in the transition period with milk production and early-lactation reproductive performance
Bibliographic record
Abstract
The objective was to examine the associations of peripartum concentrations of nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and calcium with milk production in early lactation and pregnancy at the first artificial insemination (AI) across different management systems. Fifty-five Holstein freestall dairy herds located across the United States and Canada were visited weekly for blood sample collection from 2,365 cows. For each week of sampling (from wk -1 through wk 3 relative to calving) and for each metabolite, serum concentrations were dichotomized at various thresholds to identify the thresholds with the best negative associations with milk production and pregnancy at first AI. These thresholds were used to categorize the serum concentrations into higher and lower risk categories. Repeated-measures ANOVA and multivariable logistic regression were conducted for milk production and pregnancy at the first AI data, respectively, considering cow as the experimental unit and herd as a random effect. In the week before calving, serum NEFA ≥ 0.5 mEq/L, BHBA ≥ 600 μmol/L, and calcium ≤ 2.1 mmol/L were associated with 1.6 to 3.2 kg/d milk loss across the first 4 Dairy Herd Improvement Association (DHIA) milk tests. High levels of NEFA and BHBA in wk 1 and 2 after calving (≥ 0.7 and ≥ 1.0 mEq/L for NEFA, and ≥ 1,400 and ≥ 1,200 μmol/L for BHBA), and low levels of calcium (≤ 2.1 mmol/L) in wk 1, 2 and 3 after calving were associated with milk loss at the first DHIA milk test. Serum concentrations of NEFA and BHBA were not associated with pregnancy at first AI in any sampling week, whereas calcium <2.2 to 2.4 mmol/L from wk 1 through wk 3 postpartum were associated with reduced pregnancy at first AI. In conclusion, high serum concentrations of NEFA, BHBA, and low concentrations of calcium around parturition were associated with early lactation milk loss, and low calcium concentration around parturition was associated with impaired early lactation reproduction.
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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 | 0.000 | 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".