755 Beef: From a good source of nutrients to a functional food
Bibliographic record
Abstract
Invited speaker- CSAS symposium: Healthy animals producing healthy foods. Let food be thy medicine” is a quote attributed to Hippocrates. Moving a link down the food it might be more appropriate to say “Let feed be medicine for thy food and thy food be medicine for you”. We live in times when links between diet and health are being recognized more and more as the first line of support to promote longevity and quality of life in later years. The content and composition of nutrients in feeds can play a role in animal health and productivity, and in turn the content and composition of animal derived foods can influence human health and wellness. The objectives of the current presentation will be to outline the effects of diet on the nutrient content and composition of animal derived foods, and to review how animal derived foods can functionally contribute to human health and wellness. Emphasis will be on the content and balance of omega-3 and -6 fatty acids in beef, the potential for accumulation of polyunsaturated fatty acid biohydrogenation products in beef, and our current understanding of how these may influence consumer health and wellness.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".