Exploring the benefits and challenges of establishing a DRI-like process for bioactives
Bibliographic record
Abstract
Bioactives can be defined as: "Constituents in foods or dietary supplements, other than those needed to meet basic human nutritional needs, which are responsible for changes in health status" (Office of Disease Prevention and Health Promotion, Office of Public Health and Science, Department of Health and Human Services in Fed Reg 69:55821-55822, 2004). Although traditional nutrients, such as vitamins, minerals, protein, essential fatty acids and essential amino acids, have dietary reference intake (DRI) values, there is no such evaluative process for bioactives. For certain classes of bioactives, substantial scientific evidence exists to validate a relationship between their intake and enhanced health conditions or reduced risk of disease. In addition, the study of bioactives and their relationship to disease risk is a growing area of research supported by government, academic institutions, and food and supplement manufacturers. Importantly, consumers are purchasing foods containing bioactives, yet there is no evaluative process in place to let the public know how strong the science is behind the benefits or the quantitative amounts needed to achieve these beneficial health effects. This conference, Bioactives: Qualitative Nutrient Reference Values for Life-stage Groups?, explored why it is important to have a DRI-like process for bioactives and challenges for establishing such a process.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".