Risk factors for bacteriological quality of bulk tank milk in Prince Edward Island dairy herds. Part 2: Bacteria count-specific risk factors
Bibliographic record
Abstract
A case-control study was conducted to identify specific on-farm risk factors that influence bacteriological quality of bulk tank milk in Prince Edward Island dairy herds. Total aerobic (TAC), preliminary incubation (PIC), laboratory pasteurization (LPC), and coliform (CC) counts were used to assess the bacteriological quality of bulk tank milk. Four case-control groups were defined based on the last 6 results of each test before on farm evaluation. A herd was classified as a TAC, PIC, or CC case when the herd had at least 4 high TAC, PIC, or CC counts out of the last 6 analyses for each test, respectively. For the LPC case group, a herd was required to have at least 3 high results out of the last 6 analyses. Control groups had low counts in the last 6 analyses for each test in the corresponding case group (TAC, PIC, CC, and LPC). The results of the study showed that TAC and PIC were mainly associated with cow and stall hygiene: washing the teats with water, not using teat predip, and dirty teats were risk factors. The LPC and CC were related to equipment hygiene, with high counts being associated with low temperature of the cleaning solution, high water-hardness score, and high alkalinity of alkaline detergent wash. Based on the findings of this study it can be concluded that TAC, PIC, LPC, and CC counts are of considerable value in identifying practices that could influence milk quality.
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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 | 0.005 | 0.002 |
| 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".