Unlocking the Nutraceutical Potential of Legumes and Their By-Products: Paving the Way for the Circular Economy in the Agri-Food Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Legumes, including beans, peas, chickpeas, and lentils, are cultivated worldwide and serve as important components of a balanced and nutritious diet. Each legume variety contains unique levels of protein, starch, fiber, lipids, minerals, and vitamins, with potential applications in various industries. By-products such as hulls, rich in bioactive compounds, offer promise for value-added utilization and health-focused product development. Various extraction methods are employed to enhance protein extraction rates from legume by-products, finding applications in various foods such as meat analogs, breads, and desserts. Moreover, essential fatty acids, carotenoids, tocols, and polyphenols are abundant in several residual fractions from legumes. These bioactive classes are linked to reduced incidence of cardiovascular diseases, chronic inflammation, some cancers, obesity, and type 2 diabetes, among other relevant health conditions. The present contribution provides a comprehensive review of the nutritional and bioactive composition of major legumes and their by-products. Additionally, the bioaccessibility and bioavailability aspects of legume consumption, as well as in vitro and in vivo evidence of their health effects are addressed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it