Comparison of cow milk components between daily actual and AM-PM composite samples from 2 consecutive milkings by Dairy Herd Improvement
Bibliographic record
Abstract
This study was undertaken to evaluate the level of agreement of milk fat, protein, lactose, MUN, and SCC concentrations between daily actual and AM-PM composite milk samples taken at 2 consecutive DHI milkings and to assess factors affecting their level of agreement. Milk samples from 2 consecutive milkings were collected using in-line milk meters on 4,340 Holstein cows in 100 Canadian commercial dairy herds. Three milk samples per cow were analyzed for major components: (1) evening samples; (2) morning samples; and (3) an AM-PM composite sample obtained by visually mixing equal volumes of milk from 2 consecutive milkings. Daily actual milk component concentrations were computed proportionally to milk yields. Equal 50:50 composite milk component concentrations were the average of evening and morning samples. Concordance correlation coefficients (CCC) between actual and equal 50:50 samples varied from 0.997 to 1.000. For the comparison between daily actual and AM-PM composite milk component concentrations, CCC ranged from 0.911 to 0.964. This suggested that AM-PM composite samples were not always composed of an equal 50:50 volume of milk from evening and morning milkings. A great variation of CCC was observed between herds, indicating differences in their assessment of volumes of milk to pour into vials at each milkings. Assuming that milk samples were homogeneous, AM-PM composite samples predicted daily actual milk components with great precision and accuracy. However, this was not the case in all herds. Visually assessing milk volumes to pour into vials when creating AM-PM composite milk samples was the major cause of a decrease in level of agreement when predicting daily actual milk component concentrations, which varied between herds. One recommendation might be to add indicators on DHI vials to guide in mixing an equal milk volume from 2 consecutive milkings. Because DHI records are used in decision making, it is important that predicted daily milk component concentrations are as close as possible to daily actual milk component concentrations. Producers can make an informed decision on which sampling scheme to chose according to their management objectives.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".