Introduction to <i>Cereal Chemistry</i> 2022 focus issue on proteins from grains
Bibliographic record
Abstract
I am pleased to introduce the January/February 2022 Focus Issue 99 (1) of Cereal Chemistry on Proteins from Grains. This theme was chosen to recognize the rapidly increasing interest worldwide in incorporating more plant proteins in the human diet. Concerns about health, environmental sustainability of food production, animal welfare, and that animal protein may be a limited resource are driving the trend of increasing utilization of proteins from plants to diversify food sources. Nevertheless, there are still many important research questions on how plant proteins can add value to the agri-food systems, for example, on “green” methods for processing raw materials and how to incorporate isolates into flavorsome and nutritious products attractive to consumers. These topics are addressed in the 17 articles that make up Focus Issue 2022. There are reviews on protein isolates from peas, oats, and dry beans, and on textured proteins from wheat and pea for meat alternative applications. The 13 research papers present a broad range of new science about protein isolates from various plant sources (lentils, rice bran, oat, black bean, pea, soybean, intermediate wheatgrass), different methods of preparation and processing, and structure and functional properties, including nutrition. I congratulate and thank sincerely the Guest Editors, Clifford Hall (South Dakota State University) and Michael Nickerson (University of Saskatchewan), for assembling an excellent Focus Issue, which I commend to the readers of Cereal Chemistry. I am confident this issue will make a valuable scientific contribution to proteins and grains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.165 | 0.125 |
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".