Effect of intramammary infection with non-aureus staphylococci in early lactation in dairy heifers on quarter somatic cell count and quarter milk yield during the first 4 months of lactation
Bibliographic record
Abstract
A longitudinal study was conducted to assess to what extent intramammary infection (IMI) with non-aureus staphylococci (NAS) within the first 4 d after calving in dairy heifers affects quarter milk yield (qMY) and quarter milk somatic cell count (qSCC) during the first 4 mo of lactation. In total, 324 quarters from 82 Holstein Friesian heifers from 3 commercial dairy herds equipped with an automatic milking system were included and followed from calving up to 4 mo in lactation. The automatic milking system allowed us to precisely determine the daily qMY. A milk sample from each quarter was collected in early lactation (between 1 and 4 d in milk) for bacteriological culturing and measurement of the qSCC. Subsequently, milk samples were taken on a biweekly basis for measurement of the qSCC. The milk prolactin level in early lactation was measured, and the relation with NAS IMI was determined. Overall, NAS IMI in early lactation caused only a slight but significant increase in qSCC compared with milk from noninfected quarters during the first 4 mo in lactation, whereas no significant difference in daily qMY was present between NAS-infected and noninfected quarters. The milk prolactin level in early lactation did not differ between NAS-infected and noninfected quarters either. Our data suggest that IMI with NAS (as a group) present shortly after calving do not have an adverse effect on later production. The milk prolactin concentrations were not dissimilar between NAS-infected and noninfected quarters and thus cannot explain why NAS-infected quarters do not produce less than noninfected quarters.
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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".