Amiata donkey body conformation, udder characteristics, and their relationship with milk yield and quality
Bibliographic record
Abstract
To date, no selective actions have been taken to improve milk traits in dairy donkeys, and the characteristics of the udder are not well defined in relation to the productive characteristics. This study aimed at increasing knowledge on Amiata dairy donkey body conformation, udder traits, and their relationship with milk yield and quality. Morphological, udder, and teat measurements and milk evaluations of 45 pluriparous jennies were carried out. The average wither height of the jennies was 126 cm and the chest girth was 148 cm; a large standard deviation of some body measurements was found. Forty-nine percent of the animals showed a moderately developed udder, while most of the jennies had symmetrical half-udders (96%) and the intermammary cleft was clearly visible in 53% of subjects. Correlation analysis indicated that bigger animals tend to have bigger udders, higher teat diameter, and greater distance between teat tips. A positive correlation between the teat length and the milk fat was found ( p < 0.01), which suggests that jennies with longer teats have a better ability to release milk fat. The results of this paper may be useful to define the characteristics of the milking device and address selective choices of the animals.
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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) | 0.002 | 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".