Analysis of the Relationship between Wash Water Quality and Bulk Tank Milk Quality on Ontario Dairy Farms
Bibliographic record
Abstract
The Ontario dairy industry takes great pride in providing a high quality food product to the consuming public. Milk and milk products are an important part of the daily diet of most Ontario residents. Bacterial contamination of raw milk has a major negative impact on milk quality (Reed and Grivetti, 2000). Even though most milk is pasteurized prior to consumption, raw milk is consumed by some farming families and is used to manufacture some food products (Hegarty et al, 2002). One potential source of bacterial contamination of raw milk bacteria is the water used to clean the milking and storage equipment (Jayarao and Wang, 1999). The Canadian Quality Milk (CQM) program has established criteria for water quality on dairy farms. Initial testing has suggested that as many as half of Ontario dairy farms do not currently meet these water quality standards. Test results suggest that while some water may be contaminated at its origin, there is also significant contamination that occurs in the "plumbing", and is expressed as an elevation in bacteria counts at the point-of-use. The Dairy Farmers of Ontario (DFO) have begun a water quality-testing program, intended to decrease the risk of bacterial contamination of raw milk. The net benefit to the dairy industry will be to evaluate and document the improvement in raw milk quality attributable to compliance with the CQM program. The objective of the current study was to identify areas of high risk of wash water contamination and to investigate the relationship between bacteria-contaminated wash water and elevated Bactoscan bacteria counts in raw milk.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".