Towards an Optimum Mixing Protocol for On-Farm Bulk Milk Sampling
Bibliographic record
Abstract
This paper describes the application of a bottom-sampling technique to dynamically monitor creaming and mixing in bulk milk tanks and determine optimum mixing protocols for milk sampling. Creaming onset in field experiments occurred between 40 and 50 min. Bottom-sampling data determined after 3 h of creaming indicate that the mean mixing time required to ensure a homogenous sample for composition testing is 57 s, and there is a less than 1% probability that an individual tank would require more than 2 min of agitation. Bottom and top-sampling statistics determined after 1 h of creaming indicate mean mixing times of 20 and 34 s, respectively, and predict that individual tank mixing times will exceed 46 and 64 s, respectively, less than 1% of the time. Bacterial cell counts were directly correlated with fat content, but somatic cell counts were independent of fat content. Based on these results, it is recommended that hourly agitation of bulk tanks as currently prescribed in many jurisdictions should be maintained, but the duration of intermittent agitation should be reduced from 5 to 2 min to reduce the impact of agitation on fat globule stability. If hourly agitation is effected during milk storage, agitation time before sampling can be reduced from 5 to 2 min. This will save time for drivers and trucks and reduce the potential impact of agitation on fat globule stability.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".