MONITORING OF QUARTER HEALTH STATUS BY PERIODIC MILK CONDUCTIVITY MEASUREMENT: A USEFUL MANAGEMENT TOOL IN DAIRY HERDS
Bibliographic record
Abstract
From September 2000 to July 2001 foremilk electrical conductivity (EC) was measured monthlyin 3 herds with 46, 60 and 350 milking cows, respectively. The bulk milk somatic cell count waslower than 200,000 cells per ml. A total of 16,606 quarter EC readings were taken on foremilkprior to the udder cleaning routine with a handheld conductometer. Clots and other indicators ofabnormal milk were visually detected on a black plate connected to the conductometer. Everythree months quarter foremilk was sampled for cyto-bacteriological analysis. A total of 675, 614and 4,545 samples was collected in herds A, B and C, respectively. In herds A and B, acomparison between California-Mastitis-Test (CMT) and EC was made based on the same milksamples. Results of cyto-bacteriological analyses were classified according to the standards formastitis classification of the German Veterinarian Society (over 100,000 somatic cells per ml milkand a positive bacteriological result indicates mastitis). As expected, CMT showed more affectedquarters than EC measurement, i. e. 89 % and 74 % of all mastitis quarters, respectively. On theother hand, EC measurement has some advantage over CMT: simpler handling, no chemicalsneeded, and objective numeric results obtained. In addition, EC readings and reading changes percow and quarter can be graphically evaluated, as well as herd averages. As observed in ourinvestigation, this might be a useful additional management tool especially in larger herds.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".