Diet, fruit and vegetables and One Health: benefits for health, environment, society and the consumer—proceedings of the 9th edition of EGEA conference
Bibliographic record
Abstract
PURPOSE: To present the outcomes of the EGEA Conference on the state of knowledge regarding the contribution of diets rich in fruit and vegetables (FV) to human and planetary health, commonly included in the One Health concept. METHODS: The 9th edition of EGEA Conference (20-22 September 2023, Barcelona) provided a transversal and multidisciplinary perspective on the contribution of FV to One Health, in particular to the health of individuals, society and the planet. Nearly 150 international scientists and stakeholders discussed the current state of knowledge. These proceedings are based both on a literature review and the scientific studies presented by the speakers. RESULTS: Scientific evidence confirms the role of FV in preventing cardiovascular diseases and type 2 diabetes; more evidence is needed on the effects and mechanisms of FV in cancer prevention. FV production and consumption helps ensure territorial cohesion and provides a denser, nutrient-rich diet with less environmental impact (except water use) than other food groups, but use of synthetic pesticides in FV production remains a challenge that could be addressed with agro-ecological solutions. Various factors influence consumer choice and behaviour towards FV consumption across the lifespan, with specific periods being more conducive to change. New research is emerging on the role of FV consumption in regulating gut microbiota and on both mental and brain health; the potential role of FV production and supply in tackling biodiversity loss and climate change; and better monitoring of FV consumption. CONCLUSION: Sufficient evidence confirms the contribution of diet rich in FV to One Health, with some emerging research on this topic. Concerted actions are required towards an increased consumption of FV and a more diversified and environmentally neutral FV production.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".