Effect of Calving Interval on Milk Yield in Italian Buffalo Population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it