Integrating nutrition science and consumer behaviour into future food policy
Bibliographic record
Abstract
The session 'Advancing risk assessment science - Nutrition' at EFSA's third Scientific Conference 'Science, Food and Society' aimed to foster the ongoing debate on the extent to which single nutrients, whole foods and overall diets may impact human health in wealthy populations, and to explore how societal and technological developments could affect food choices and diets in the future. The overarching goal of the session was to discuss how dietary guidelines could evolve to account for the switch from single nutrient deficiencies to diseases of malnutrition in all its forms as the predominant public health concern in developed countries. Speakers addressed the contribution of single nutrients to the prevalence of chronic metabolic diseases, discussed the need to move towards diets focusing on whole foods and overall eating patterns, provided insides on food innovation and consumer behaviour and stressed the need for multidisciplinary approaches to face these challenges.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".