The Effect of Pathogen-Specific Clinical Mastitis on the Lactation Curve for Somatic Cell Count
Bibliographic record
Abstract
Data from 274 Dutch herds recording clinical mastitis (CM) over an 18-mo period were used to investigate the effect of pathogen-specific CM on the lactation curve for somatic cell count (SCC). Analyzed pathogens were Staphylococcus aureus, coagulase-negative staphylococci, Escherichia coli, Streptococcus dysgalactiae, Streptococcus uberis, other streptococci, and the culture-negative samples. The dataset contained 178,754 test-day records on SCC, recorded in 26,411 lactations of 21,525 cows of different parities. In lactations without both clinical and subclinical mastitis, SCC was high shortly after parturition, decreased to a minimum at 50 days in milk (DIM), and increased slowly toward the end of the lactation. Effects of CM on lactation curves for SCC differed among the pathogens isolated. Before a case of clinical E. coli mastitis occurred, SCC was close to the SCC of lactations without both clinical and subclinical mastitis, and after the case of CM had occurred, SCC returned rather quickly to a low level again. Similar curves were found for lactations with cases of CM associated with culture-negative samples. Before a case of clinical Staph. aureus mastitis occurred, average SCC was already high, and it remained high after the occurrence. Effects of CM associated with Strep. dysgalactiae, Strep. uberis, and other streptococci on the lactation curve for SCC were comparable. They showed a continuous increase in SCC until the case of pathogen-specific CM occurred, and afterwards SCC stayed at a higher level. Using SCC test-day records, these typical characteristics of each pathogen may be used to find more effective indicators of CM.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".