Significance of Nutritional Effects on the Freezing Point of Milk Ontario Agri-Business Association Nutrition Committee
Bibliographic record
Abstract
Milk freezing point estimates are used by Dairy Farmers of Ontario (DFO) to indicate milk with water added. Average milk freezing point is –0.540 oHortvet (H). If the freezing point estimate is>-0.530 oH in a milk sample a warning is provided to the producer. At a freezing point test>-0.525 oH a financial penalty is imposed on the producer. High milk freezing point (penalty level) is widely recognized to be caused in almost all cases by freezing of the milk during cooling, or water added to the milk due to rinse water going into the tank or water added to the tank. An unbalanced ration, including factors such as low energy or lack of grain, and lack of salt or minerals are frequently cited as other possible causes of high milk freezing point when problem cases are investigated. Little research has been published in the past 20 – 30 years on the effect of feeding on milk freezing point. Reviews of older published research (3 and 6) indicate that nutrition can have a statistically significant effect on milk freezing point. Whether the effects of feeding on milk freezing point can be large enough to cause a milk freezing point
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".