Evaluation and Comparison of 2 On-Farm Tests for Estimating Somatic Cell Count in Quarter Milk Samples from Lactating Dairy Cattle
Bibliographic record
Abstract
BACKGROUND: The somatic cell count (SCC) is commonly used to monitor udder health and diagnose subclinical intramammary infection (IMI) in dairy cattle. HYPOTHESIS: The Somaticell test (ST) and California mastitis test (CMT) are clinically useful cow-side tests for diagnosing subclinical IMI. ANIMALS: One hundred and eleven dairy cows at dry-off and 92 cows within 4-7 days postcalving. METHODS: Quarter foremilk samples were obtained and analyzed with a DeLaval cell counter (DCC, reference method), ST, and CMT. The ST was run in a simulated cow-side manner using milk at 37°C instead of 0-8°C as recommended by the manufacturer. Test performance for diagnosing IMI (DCC SCC >200,000 cells/mL) was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and the kappa coefficient (κ) at the optimal cut-point for each test. The effect of milk/reagent temperature also was evaluated. RESULTS: Compared to the reference method, the ST run in a simulated cow-side manner had an AUC = 0.68 and κ = 0.24 at dry-off, and AUC = 0.74 and κ = 0.40 in fresh cows. The CMT performed much better than the ST in diagnosing subclinical IMI with AUC = 0.88 and κ = 0.77 at dry-off, and AUC = 0.87 and κ = 0.76 in fresh cows. The measured ST value decreased with increasing temperature of the milk/reagent mixture. CONCLUSIONS/CLINICAL IMPORTANCE: The ST is optimized for use on milk at 0-8°C and is therefore designed for on-farm use on refrigerated milk samples. The ST is not suited for use as a cow-side screening test for IMI because the milk temperature exceeds the recommended range for the test.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".