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 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 | 0.056 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".