Genetic Parameters and Sire Ranking for the Adjusted Estimate of Mozzarella Cheese Production in Italian Mediterranean Buffalo
Bibliographic record
Abstract
The objectives of this study were to investigate the effect of adjusting mozzarella cheese production (APKM) compare to the unadjusted PKM on genetic parameters and sire ranking in Italian buffalo. 337,158 lactation records at 270d, from years 1997 to 2014, up to fifth parity, of 136,525 animals were analyzed. Estimated Breeding Values (EBVs) for APKM and PKM were predicted by BLUP-Animal Model with the fixed effects of age-parity, days-open, and contemporary-group; and the random factors of animal, permanent environmental and residuals. APKM showed lower heritability (0.23) than that of PKM (0.31) which might be due to the lower genetic correlation between APKM and milk yield (MY) (0.09) compared to that of PKM with MY (0.25). APKM positively changed sire ranking with increasing rank of sire with positive EBVs of Protein and Fat percentage, and decreasing rank of sire with negative EBVs of Protein and Fat percentage. Due to the larger correlation between PKM and MY, by selecting through the PKM, one risk is to increasing the number of buffalo that produce more milk, not optimal for making mozzarella cheese. Whereas, genetic selection through the APKM will increase the number of high-yielding buffalo with positive EBVs for Protein and Fat percentage
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".