Risk factors for bacteriological quality of bulk tank milk in Prince Edward Island dairy herds. Part 1: Overall risk factors
Bibliographic record
Abstract
The objective of this study was to determine on-farm risk factors for bacteriological quality of bulk tank milk. Bulk tank raw milk quality was evaluated on all Prince Edward Island dairy herds (n = 235) over a 2-yr period (March 2005 to March 2007). Biweekly total bacterial, preliminary incubation, laboratory pasteurization, and coliform counts were conducted using a Petrifilm culture system. For the assessment of risk factors, a case-control study was conducted from January 2006 to May 2007. Case and control herds were defined based on the last 6 analyses of bulk tank bacterial counts before on-farm evaluation. Cases were herds that had multiple elevated counts for any of the parameters measured. A total of 69 herds (39 cases and 30 control herds) were evaluated. Data collection included 1) observation and questionnaire on basic hygiene and farm management practices; 2) complete wash analysis of the milking equipment, monitoring the presence of bacterial films on equipment and evaluation of cooling system function; and 3) environmental and cow hygiene scoring. Data were analyzed using multivariable logistic regression. The results of the final model indicated that high alkalinity in the wash water and poor teat-end cleanliness were associated with high bacterial counts in bulk tank milk (odds ratios = 12 and 5.3, respectively). It was also observed that high water temperature of detergent wash and the use of a water softener were associated with low bacterial counts in bulk tank milk (odds ratios = 0.87 and 0.11, respectively). A significant association between udder hair clipping and teat-end cleanliness was also observed. In conclusion, this study highlights the importance of udder hygiene and milking system washing factors on hygienic quality of bulk tank 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".