350 Enhancing the nutritional value of red meats through genetic and feeding strategies
Bibliographic record
Abstract
Abstract Consumption of red meats contributes substantially to the intake of several essential nutrients in the human diet, including protein, essential fatty acids, and several vitamins and trace minerals. Despite concerns regarding potential negative impacts on human health and the environment, demand for red meats continues to increase worldwide, particularly in developing countries. Enhancing the nutritional value of meats is essential to provide consumers with competitive products that meet their nutrient requirements and address their health concerns. Different nutrients in red meats vary in their responsiveness to dietary and genetic manipulations. The fat content and fatty acid composition of meats can be easily modified through animal nutrition. Similarly, iodine, selenium and fat-soluble vitamin content can be significantly manipulated using dietary strategies. In contrast, amino acids, copper, iron, zinc, and water-soluble vitamins are less responsive to dietary manipulations. Feeding studies (i.e. supplements, additives, production systems, life period, nutritional regimes and duration) are relatively abundant and have shown substantial changes to nutritional value in many scenarios. Traditional breeding, including genetic selection for specific traits, has been used to influence multiple carcass and meat quality attributes relevant to nutritional value of meat, including leanness. The use of molecular genetics (i.e. GWAS, identification of genetic variants, gene expression profiles) in the past decade has offered alternative genetic perspectives on improving the nutritional value of meats. Recent studies have also shown influence of rumen or gut microbiome features on nutrient bioavailability and utilization. However, a limited number of studies have combined feeding strategies for specific genetic potential. Given the nutrient composition of meat can be influenced by a dynamic interaction of genetics and environment, the fields of phenomics, nutrigenomics, integrative diet-host-microbiome, and systems biology may bring further insight to better understand the manipulation of red meat composition under both experimental and commercial conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".