Effect of pre-stimulation on milk flow pattern and distribution of milk constituents at a quarter level
Bibliographic record
Abstract
The aim of this study was to investigate milk flow patterns and milk composition in relation to pre-milking udder stimulation. The milk of one quarter of each of the sixteen cows was removed separately and in the course of milking it was divided into six fractions (P âĂĂ cisternal milk during milking without stimulation and the first 300 ml during milking with pre-stimulation, 0-25%, 25-50%, 50-75%, 75-100%, 75-100%, MS-machine stripping) and into five portions (25%, 50%, 75%, 100%, 100% + MS). Two milkings were performed during two consecutive evening milkings with or without manual stimulation. Pre-stimulation resulted in a reduction of milking time, duration of the increase and decline phase of milk flow, stripping yield, but it increased the peak flow rate as compared to milking without pre-stimulation (P < 0.05). In both fractions and portions the content of fat increased steadily during milking and reached a maximum at MS. Lactose increased from P to 50-75% and then it decreased to MS. Significantly higher fat contents at 25% and 50% portions and in both protein and dry matter at 25% portions were found during milking with pre-stimulation as compared to no stimulation (P < 0.05). The content of fat, protein and dry matter were also higher in both P and 0-25% fractions for milking with pre-stimulation (P < 0.05). Pre-stimulation positively influenced the parameters of milk flow and therefore the efficiency of milk removal and contributed to better distribution of components in milk fractions during milking.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".