Test characteristics from latent-class models of the California Mastitis Test
Bibliographic record
Abstract
We evaluated (using latent-class models) the ability of the California Mastitis Test (CMT) to identify cows with intramammary infections on the day of dry-off. The positive and negative predictive values of this test to identify cows requiring dry-cow antibiotics (i.e. infected) was also assessed. We used 752 Holstein-Friesian cows from 11 herds for this investigation. Milk samples were collected for bacteriology, and the CMT was performed cow-side, prior to milking on the day of dry-off. At the cow-level, the sensitivity and specificity of the CMT (using the four quarter results interpreted in parallel) for identifying all pathogens were estimated at 70 and 48%, respectively. If only major pathogens were considered the sensitivity of the CMT increased to 86%. The negative predictive value of the CMT was >95% for herds with major-pathogen intramammary-infection prevalence <15%, so that selective dry-cow therapy might be reasonable for such herds if cows were screened with the CMT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".