Plant Proteins: Methods of Quality Assessment and the Human Health Benefits of Pulses
Bibliographic record
Abstract
As countries increase their standard of living and individual income levels rise, there is a concomitant increase in the demand for animal-based protein. However, there are alternative sources. One of the alternatives available is that of increased direct human consumption of plant proteins. The quality of a dietary protein is an important consideration when discussing the merits of one protein source over another. The three most commonly used methods to express protein quality are the protein efficiency ratio (PER), a weight gain measurement; protein digestibility-corrected amino acid score (PDCAAS); and the digestible indispensable amino acid score (DIAAS). The possibility that alterations in the quality and quantity of protein in the diet could generate specific health outcomes is one being actively researched. Plant-based proteins may have additional beneficial properties for human health when compared to animal protein sources, including reductions in risk factors for cardiovascular disease and contributions to increased satiety. In this paper, the methods for the determination of protein quality and the potential beneficial qualities of plant proteins to human health will be described.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".