Incidence of clinical mastitis and distribution of pathogens on large Chinese dairy farms
Bibliographic record
Abstract
Knowledge of the incidence of clinical mastitis (CM) and the distribution of pathogens involved is essential for development of prevention and control programs as well as treatment protocols. No country-wide study on the incidence of CM and the distribution of pathogens involved has been conducted in China. Core objectives of this study were, therefore, to determine the cumulative incidence of CM and the distribution of pathogens causing CM on large Chinese (>500 cows) dairy farms. In addition, associations between the distribution of CM pathogens and bedding materials and seasonal factors were also investigated. Bacterial culture was done on a total of 3,288 CM quarter milk samples from 161 dairy herds (located in 21 provinces) between March 2014 and September 2016. Additional data, including geographical region of herds, herd size, bedding types, and number of CM cases during the last month, were also recorded. Mean cumulative incidence of CM was 3.3 cases per 100 cows per month (range = 1.7 to 8.1). The most frequently isolated pathogens were Escherichia coli (14.4%), Klebsiella spp. (13.0%), coagulase-negative staphylococci (11.3%), Streptococcus dysgalactiae (10.5%), and Staphylococcus aureus (10.2%). Streptococcus agalactiae was isolated from 2.8% of CM samples, whereas Streptococcus uberis were isolated from 2.1% of samples, and 15.8% of 3,288 samples were culture-negative. Coagulase-negative staphylococci, E. coli, and other Enterobacter spp. were more frequently isolated in the northwest than the northeast or south of China. Streptococcus dysgalactiae, other streptococci, and Strep. agalactiae were more frequently isolated in winter (October-March), whereas E. coli and Klebsiella spp. were mostly isolated in summer (April-September). Streptococcus dysgalactiae was more often isolated from CM cases of herds using sand bedding, whereas Klebsiella spp. and other streptococci were more common in herds using organic bedding. The incidence of CM and distribution of pathogens differed among herds and better mastitis management is needed. Furthermore, geography, bedding materials, and season should be included when designing mastitis control and prevention schemes for Chinese dairies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".