Sources of variation and heritability estimates for milking speed in Italian Brown cows
Bibliographic record
Abstract
Milking speed is defined as the time required to milk a cow (Bowman et al., 1996). Milkability can be considered an important functional trait in dairy cattle for udder health (Dodenhoff et al., 1999) and workability (Visscher and Goddard, 1995). Sivarajasingam et al. (1984), found milking speed to be the third most important trait on net profit for dairy farms, after milk yield and fat content, so much so that it is included in breeding programmes for dairy cattle (Schneeberger and Hagger, 1985; Boettcher et al., 1997). Different methods to measure milkability traits, subjective and instrumental, were reported in the literature. Meyer and Burnside (1987) found a high genetic correlation between a subjective evaluations of milking speed of cows by Canadian farmers (using five classes: 1 = “very slow” ... 5 = “very fast”), with total milking time by chronometer.......
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".