Nutrition and sustainable food system issues: Nutrition, Chemical Food Safety & Consumer Behaviour division: ALIMH
Bibliographic record
Abstract
Marie-Jo Amiot-Carlin, from INRA, presented on the work of her division (400 permanent staff), on eating behaviours, health function, nutrients, microbiota, toxicological aspects, etc. They work in Quebec, Brazil, India, through joint international laboratories. There is a need now to talk about food and nutritional security, not just food security. In the nutrition transition context, in terms of the balance between plant and animal proteins, as well as protein quality issues, the quality of absorption of nutrients is important. Regarding micronutrient deficiencies and hidden hunger, there is a role of some compounds (anti-nutritional) for the bioavailability of vitamins in the whole diet. Finally, there is a role of the microbiome in chronic under-nutrition, and to counteract stunting. We need a double strategy: microbiota and nutrition interventions. Moreover, basic research in nutrition could help to orientate agriculture toward an nutrition-sensitive agriculture and sustain food systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.012 |
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".