Enhancing the vitamin content of meat and eggs: Implications for the human diet
Bibliographic record
Abstract
Enhancing the vitamin content of meat and eggs provides an opportunity to increase the levels of key nutrients-especially those deemed to be at marginal or insufficient levels-in the human diet for optimal health and well-being. In general, enhancement efforts have focussed on developing feeding strategies to achieve optimal vitamin levels in meat and eggs. The definition of an optimal strategy is influenced by factors such as: (1) the efficiency of vitamin transfer into the final product, (2) the impact on animal performance or health, (3) the impact on the quality characteristics of the final product and (4) economic considerations. Vitamins are an extremely diverse class of nutrients in terms of their chemical and physical properties. Each vitamin differs with respect to stability during processing, susceptibility to bioconversion within the intestinal tract, digestibility, transport and storage in tissues. It follows that the development of vitamin-enriched meat and eggs will be highly dependent on the interaction of multiple factors. Ultimately, the success of such strategies must be judged against the contributions that the enriched products make to the human diet in terms of vitamin intake and consumer acceptance of the products. Key words: Meat, eggs, vitamin enhancement, bioavailability, dietary reference intakes
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".