Effect of subclinical intramammary infection on somatic cell counts and chemical composition of goats' milk
Bibliographic record
Abstract
We investigated effects of subclinical intramammary infection (IMI) on milk somatic cell count (SCC) and milk composition in udder halves of dairy goats. A total of 35 mixed-age Alpine does (70 udder halves; approximately 55 kg body weight) were rotationally grazed on a mixture of vegetative forages (wheat/berseem clover, sudan grass and cowpeas). Milk samples for bacterial analysis and SCC were collected monthly from both halves from April to September, 2001. Across stages of lactation, 19-31% of udder halves became infected. The prevalence of IMI exhibited quadratic patterns through multi-peaked responses within each stage of lactation. Higher rates of IMI were observed during the early stage of lactation (19% in May) and in the late stage of lactation (31% in September). Coagulase negative Staphylococcus (CNS, 43.7%), Staph. aureus (35.4%), and Pseudomonas aeruginosa (12.4%) were the most prevalent pathogens. Within single-strain IMI, log SCC (6.24) was lower (P<0.01) for CNS than those derived from IMI by Staph. aureus (6.49), Ps. aeruginosa (6.53) or Serratia spp. (6.90). Infected udder halves had a higher average SCC (4761 v. 2259 x 10(3) cells/ml; P<0.01) than uninfected halves, but uninfected halves often had similar levels of SCC to infected halves. Daily average milk production was not significantly different between infected and non-infected goats and the relationship between IMI and SCC was not always correlated. Effective mastitis screening requires bacteriological culture since SCC was not highly correlated.
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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) | 0.001 | 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".