A "Fork-to-Farm" Multi-Scale Approach to Promote Sustainable Food Systems for Nutrition and Health: A Perspective for the Mediterranean Region
Bibliographic record
Abstract
Mediterranean countries are undergoing dietary and nutritional changes that affect their inhabitants' health, while facing massive environmental challenges. The increasing demand of water in agriculture, the capacity to maintain local food production, and the growing dependence on food imports are interconnected issues that must be addressed to ensure food security and nutrition in the Mediterranean region. Here, we present the conceptual framework and methodologies developed by the MEDINA-Study Group for rethinking food systems toward sustainable consumption and production modes. Based on its multidisciplinary expertise, the MEDINA-Study Group designed a "fork-to-farm" multi-scale approach, stemming from current dietary habits and examining how some options to nutritionally improve these habits might affect the food systems. This approach was developed for research activities in the South of France and Tunisia, two areas with very different diet-agriculture-environment nexus. The conceptual framework is based on the analysis of elements of the food systems (from consumption to production) at different levels (individual, household, regional and national levels). The methods include: (i) modeling options of dietary changes at different scales, in order to nutritionally optimize food consumption-production without increasing the environmental impact, (ii) translating the best-choice changes into possible policy actions, (iii) testing the acceptability and feasibility of these actions with several stakeholders, and (iv) producing guidelines for sustainable food choices and production. The MEDINA-Study Group identified additional issues that could be included in a future framework to help designing ambitious agricultural, food and health policies in the Mediterranean region.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".