Estimation of milk lactose and somatic cells for the diagnosis of sub-clinical mastitis in dairy buffaloes
Bibliographic record
Abstract
The objective of the study was to estimate milk lactose and somatic cells in normal and sub-clinically affected dairy buffaloes under field conditions. Four hundred quarter samples from 100 healthy dairy buffaloes were subjected to Surf Field Mastitis Test (SFMT), direct microscopic somatic cell count (SCC) and milk lactose content analysis. Twenty quarters positive and negative samples for SFMT were subjected to microbiological examination. Mean milk SCC at SFMT score N (negative), T (traces), first degree positive (P1), second degree positive (P2) and third degree positive (P3) were 2.06 × 10 5 , 3.73 × 10 5 , 9.69 × 10 5 , 31.97 × 10 5 and 121 × 10 5 mL -1 respectively. Mean milk lactose at SFMT score N, T, P1, P2 and P3 was 5.10, 4.81, 4.66, 3.92 was 2.66%, respectively. Out of twenty quarter samples with negative score of SFMT, 16 quarter samples showed no growth; whereas, gram negative cocco-bacilli were also isolated from two quarter sample and from two quarter samples mixed growth was observed. Out of twenty quarter samples with positive score of SFMT Staphylococcus aureus was isolated from 10 quarters samples, Streptococcus agalactiae from three quarters, mix growth of S. aureus and S. agalactiae from three quarter samples and from four quarter samples coagulsae negative staphylococci (CNS) were isolated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".