Changes of physicochemical indicators during mastitis and the effects of milk ejection on their sensitivity
Bibliographic record
Abstract
We examined the relationship between physicochemical indicators and somatic cells in the milk of dairy cows during experimentally induced mastitis and their significance as indicators for use in controlling udder health. We were concerned particularly with the effect of alveolar milk ejection on the sensitivity of these indicators. In Expt 1, Escherichia coli lipopolysaccharide (Esch. coli LPS) was injected into the left rear quarter to induce an inflammatory reaction in one quarter in each of six cows. The contralateral control quarter was injected with a solution of NaCl (9 g/l). Nine milk samples were taken from both quarters until 60 h after injection. In Expt 2, repeated milk samples were taken every 20 s from one quarter during a 120-s teat stimulation in 20 cows with different somatic cell counts (SCC). Quarters were clustered for low (<5.0 log cells/ml), mid (5.0-5.7 log cells/ml) and high (>5.7 log cells/ml) SCC of the sample taken at t=0 s. Samples were analysed for SCC, electrical conductivity (EC) and Na+ and Cl- concentrations. During the experimental inflammation SCC, EC, Na+ and Cl- peaked at 12 h from LPS administration and values in treated quarters (T) at this time were elevated to 7900, 157, 501 and 169% of the values in untreated quarters, respectively. In Expt 2, SCC, EC, Na+ and Cl- in high SCC quarters were 2520, 121, 283 and 141% of low SCC quarters at the start of stimulation (t=0 s), respectively. Highly significant (P<0.001) differences in EC, Na+ and Cl- between high and low SCC quarters disappeared owing to the onset of alveolar milk ejection 100 s after the first contact with the teat. In conclusion, SCC in cows' milk provided the strongest amplitude in the case of an intramammary inflammation. EC, Na+ or Cl- were useful tools only if the measurements were performed in cisternal milk before the start of alveolar milk ejection.
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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".