Bibliographic record
Abstract
Between 1961 and 2018 on the total number of dairy ruminants, the percentage of buffaloes and the quantity of milk produced by them had an increasing trend from 3.7% to 5.2% and from 5.4 to 15.7%, respectively; this value on the world total "cow milk + buffalo milk" rose to 18.64%. In the Asian continent, buffaloes' incidence on the total number of dairy ruminants increased slightly (from 10.2% to 11.7%) while the percentage of cattle fell from 38% to 26.4%. In the same period, the percentage of buffalo milk decreased from 44.6% to 36.9%, while cow milk shares increased from 55.4% to 63%. The percentage of milk produced in Pakistan, India, Nepal (after 2010), and especially in Egypt showed a downward trend. In Italy, the trend has always been increasing. The market price of buffalo milk in developing countries does not compensate for costs which are 15% higher than cow's milk, if only the cost of feeding is considered, and increases to 40% when the difference in kg of milk equivalent between the two species is about 900 kg per lactation. A reduction of the production gap between the two species is difficult to be achieved because the genetic improvement of dairy cattle is performed in industrialized countries with higher financial support. Therefore, a marketing effort is needed to make well-differentiated buffalo products with specific and well-received sensory properties. In this regard, it should also be noted that δ-valerobetaine, a bioactive molecule beneficial for human health, is present in buffalo milk and meat in higher quantities than in cow's milk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".