Somatic cell count and mastitis pathogen detection in composite and single or duplicate quarter milk samples
Bibliographic record
Abstract
Abstract: The most acceptable criteria for diagnosing bovine intramammary infections include results of bacteriological culture and measures of inflammation. Therefore, information on the diagnostic characteristics of the procedures used to identify infected quarters is required. Thus, this study was designed to evaluate a set of criteria to classify the infectious status of an udder at the quarter (single and duplicate milk samples) and cow (composite milk sample) levels, and to compare the infectious status with somatic cell counts (SCCs) of the samples. Here, the SCC thresholds determined by receiver operating characteristic curve analysis had a higher Youden index using mammary quarter duplicate milk samples as the gold standard for testing compared with single quarter and composite milk samples, especially for samples for which at least one of the duplicates was microbiologically positive, regardless of the mastitis pathogen isolated. The kappa coefficient for bacteriological results of the single quarter milk samples (single S1 and S2) was 0.85±0.019, indicating that single quarter milk sampling can be useful in mastitis control programs. Therefore, the use of composite milk samples to detect mastitis pathogens may be limited to the detection of major pathogens, given their predictive values. Thus, our findings suggest that the milk SCCs and microbiological examinations, although regarded as the most reliable indicators of ongoing mastitis, should be used in an integrated manner in mastitis control programs. Furthermore, the accuracy of single, duplicate and composite microbiological analyses to diagnosis mastitis should be considered for its implications in mastitis control strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".