Effect of Calving Interval on Milk Yield in Italian Buffalo Population
Bibliographic record
Abstract
The objective of this study was to investigate the effect of the previous calving intervals (CI) on milk yield (MY) in the current lactation for the Italian buffalo breed population. Data for 86,585 lactation records from the Italian Buffalo Breeders Association database, were analyzed. MY BLUP-estimates were obtained by including in the Animal Model the fixed effects of age-parity, previous CI, and herd-contemporary-group. The MY solutions for the months of CI were analyzed with the linear regression model where CI in months was the explanatory variable. 59.66% of the lactation records had CI between 11 and 14 months. 37.91 % of the lactation records were distributed between 15 and 24 months. The smaller percentage of records showed CI greater than 24 months. This CI distribution may be, in part, the result of herd management strategies. Dairy producers try to shorten the CI of their herd in order to get the most profit from early conceptions of the buffalo. The regression model and its parameters were statistically significant. The coefficient of determination was equal to 0.58. The intercept was equal to 72.42 kg; and the linear coefficient (b) was equal to -3.43. The negative value of b denotes a negative effect of CI on MY. This result indicates that there is a negative linear relationship between previous CI and MY in the current lactation. Therefore, shorten the CI may increase the profits of the farm through higher MY, because it has less of a negative effect on MY than longer CI.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".