The FiberTAG project: Tagging dietary fibre intake by measuring biomarkers related to the gut microbiota and their interest for health
Bibliographic record
Abstract
Abstract The scientific rationale for dietary fibre intake recommendations comes from the recognition of their benefits for health based on studies first published many years ago. It remains unclear which are the key physiological effects generated by dietary fibre in view of the diversity of the food components considered as dietary fibre, of the relevance of their classification (soluble and insoluble) and from the recent discoveries putting forward their interactions with the gut microbiota. The project FiberTAG (Joint Programming Initiative ‘A Healthy Diet for a Healthy Life’ 2017–2020 www.fibertag.eu/ ) aims to establish a set of biomarkers (markers of gut barrier function and bacterial co‐metabolites including volatile compounds and lipid derivatives), measured in different biological compartments (faeces, blood or breath) linking dietary fibre intake and gut microbiota‐related health effects. The FiberTAG consortium brings together academic and industrial partners from Belgium, France, Germany and Canada to share data and samples obtained from existing as well as new intervention studies in order to evaluate the relevance of such biomarkers. The FiberTAG consortium is currently working on five existing cohorts (prospective observational or nutritional interventions in healthy or obese patients), and a number of new intervention studies to analyse the effect of insoluble dietary fibre (wheat bran and chitin‐glucan, provided by the industrial partners) in healthy individuals or in obese patients at high cardiometabolic risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".