Bibliographic record
Abstract
The world is demanding more protein for human consumption-increasing amounts of plant proteins are being used to meet this increasing demand. It has been estimated that global plant-based alternative protein market could swell to $162 billion in the next decade from $29.4 billion in 2020 and every tenth portion of meat, eggs, and dairy eaten around the globe by 2035 could be derived from plant proteins. White lupin (Lupinus albus L.), a legume, has been researched in Virginia for several years which has resulted in development of several winter-hardy and high yielding lines. However, concentrations of protein and relative concentrations of various amino acids in seeds of these lines are not known. Therefore, objective of this study was to characterize protein in winter-hardy lupin lines. Seeds of five winter-hardy white lupin lines grown during 2020-2021 contained about 51% protein as compared to literature values of about 35 and 24% protein in soybean and winter pea seeds, respectively. Concentrations of nine essential amino acids (isoleucine, leucine, lysine, methionine, threonine, tryptophan, valine, phenylalanine, and histidine) in lupin seed varied from 1.25 to 1.41, 1.98 to 2.51, 1.12 to 1.60, 0.21 to 0.27, 1.03 to 1.28, 0.25 to 0.30, 1.22 to 1.40, 1.14 to 1.28, and 0.69 to 0.79, respectively. These concentrations compared quite well with those in soybean and winter pea seed. These results indicate that white lupin has considerable potential to meet alternative plant protein needs of manufactures and consumers.
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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 | 0.000 | 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) | 0.001 | 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 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".