Bibliographic record
Abstract
By 2050, the world population is estimated to reach 9.6 billion, and this growth continues to require more food, particularly proteins.Moreover, the Westernisation of society has led to consumer demand for protein products that taste good and are convenient to consume, but additionally have nutritional and health maintenance and well-being benefits.Proteins provide energy, but additionally have a wide range of functions from enzymatic activities in the body to bioactivities including those associated with heart health, diabetes-type 2-prevention and mental health maintenance; stress relief as well as a plethora of other health beneficial attributes.Furthermore, proteins play an important role in food manufacture and often provide the binding, water-or oil-holding, emulsifying, foaming or other functional attributes required to ensure optimum sensory and taste benefits for the consumer.The purpose of this issue is to highlight current and new protein sources and their associated functional, nutritional and health benefits as well as best practices for quantifying proteins and bioactive peptides in both a laboratory and industry setting.The bioaccessibility, bioavailability and bioactivities of proteins from dairy, cereal and novel sources including seaweeds and insect protein and how they are measured and the relevance of protein quality measurement methods including the Protein Digestibility Amino Acid Score (PDCAAS) and Digestible Indispensable Amino Acid Score (DIAAS) are highlighted.In addition, predicted future protein consumption trends and new markets for protein and peptide products are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.023 |
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".