Evaluation of Short-Season Soybean (Glycine max (L.) Merr.) Breeding Lines for Tofu Production
Bibliographic record
Abstract
Soybean breeding programs targeting tofu quality must evaluate their performance within zones of adaptation. A comprehensive study was carried out to examine soybean breeding lines from three maturity groups (MGs; MG0, MG00, and MG000) from 2018 to 2022. Several agronomic, chemical composition and tofu-related quality traits were evaluated, and the associations among traits were investigated. The results showed that genotypes in MG0 yielded higher and matured later, which confirmed that the selection of targeted genotypes for a specific maturity group was successful. Non-imbibed “stone seeds”, an important quality trait for tofu processors, were higher in MG000 lines. Tofu texture using both GDL and MgCl2 coagulants was positively associated, indicating one coagulant might be enough for screening purposes. The MG by traits biplot showed very clear MG clustering for all genotypes tested from 2018 to 2022, signifying that the MG has a more pronounced effect on the investigated traits than the environmental effects seen in different years, regardless of the MG. Most tofu-related traits were higher and showed stronger associations in MG0 lines compared to the lines in earlier MGs, indicating a need for future effort in shorter season MGs. Overall, this study provided useful information for selecting soybean lines for tofu end-use application targeting specific MGs.
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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 | 0.001 | 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".