Genotypic and environmental effects on soybean protein composition and related effects on tofu texture and yield
Bibliographic record
Abstract
Selection for soybean ('Glycine max' L. Merrill) genotypes with improved tofu quality has largely focused on increasing seed size and seed protein content. This study was conducted to examine genotypic and environmental effects on soybean protein composition, tofu texture, and tofu yield, and to investigate the contribution of various soybean protein subunits to tofu texture and yield. Genotypes were significantly different for protein subunit content, tofu hardness and tofu yield. Location effects were significant for subunit content but did not significantly affect tofu yield nor tofu hardness. Year effects were not significant for protein subunit content, but were significant for tofu yield. The [alpha] + [alpha]1 fraction was the major determinant of tofu hardness. Models developed to explain tofu hardness using soymilk-extracted protein fractions were applicable to a larger set of genotypes, while models using seed-extracted protein fractions explained a significant portion of the variation in tofu yield. Results from this study show that selection for decreased [alpha] + [alpha]1 will improve both tofu hardness and tofu yield.
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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".