River Buffalo Meat Production and Quality: Sustainability, Productivity, Nutritional and Sensory Properties
Bibliographic record
Abstract
One of the most important challenges facing today’s society is feeding a growing world population. This review aims to examine the available information to assess the potential of river buffalo as a meat producer with a focus on the sustainability of the supply chain and on meat quality in terms of nutritional and sensory properties. Traditionally, buffalo meat came from old, culled animals in rural agricultural regions where animals were slaughtered at the end of their productive life as dairy or draught animals. Therefore, the meat had low quality. However, when younger animals are used, buffalo meat is generally well appreciated by consumers. Buffaloes can adapt to different production systems and convert poor-quality high fiber feedstuffs into high-quality products, including meat, with a lower degree of competition with human nutrition. In addition, although requiring more land, extensive production systems may have lower environmental impacts due to the low inputs used in the productive process and show higher levels of animal welfare. Although weight gains and dressing percentages are generally lower than in cattle, the meat is characterized by better nutritional properties (low fat and cholesterol contents, high-quality protein, and unsaturated fatty acids). In addition, the use of appropriate production systems might improve its sensory properties. Therefore, buffalo meat may be considered a good option to meet the increasing demand for food for human consumption.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".