DETERMINANTS OF UDDER QUARTER MILK YIELD IN AUTOMATICALLY MILKED COWS
Bibliographic record
Abstract
The objective of this paper was to analyse udder quarter milk yield in automatically milked cows, taking into account the lactation number, lactation period and season, as well as to determine the share of individual quarters (front and rear quarters, left and right quarters) in total milk yield during a single milking. The analysis was conducted using data obtained from one cattle farm with 280 Polish Holstein-Friesian (PHF) cows, milked with 4 VMS milking robots. The analysed parameters included: milk yield during milking of front quarters (left and right), rear quarters (left and right), the ratio of front quarter to rear quarter and left to right quarter yields. In the oldest cows, an increase was observed in udder quarter milk yield in lactations 1–3, followed by a decrease in lactation 4 and then another increase. Cows in lactations ≥5 have better milk yield than cows in the 3rd lactation. An average milk yield of one quarter of the udder for all lactations was 3.81 kg. With higher lactation numbers, there was a decrease in milk yield for all udder quarters. The reduction in udder quarter milk yield of lactating cows ranged from 1.52 kg (rear right quarter) to 1.78 kg of milk (rear left quarter) and from 1.59 kg (front right quarter) to 1.70 kg of milk (front left quarter). No significant differences were observed in the milk yield of individual udder quarters in the spring, summer and autumn seasons. On the other hand, there was a marked increase in this parameter in the winter months. With successive lactations, the share of front quarters in total milk yield decreased – from 46.9% in primiparous cows to 41.2% in cows after ≥5 lactations. The share of rear quarters in milk yield in turn increased. With every successive lactation, the disproportion in the milk yield between the front quarters and rear quarters widened. When it comes to the left and right quarters of the udder, in terms of the factors considered, similar results were observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".