Seed Coat Cracking of Soybean (<i>Glycine max</i> [L.] Merr.) After Soaking and Cooking
Bibliographic record
Abstract
Seed coat cracking after soaking (SCAS) and after cooking (SCAC) are unfavorable traits associated with soybeans for food uses, such as cooked and seasoned beans (nimame) and fermented steamed beans (natto) because they cause an inferior appearance of the products and clogging of the production lines. The variation and causes of SCAS and SCAC among cultivars have not yet been clarified, but if they are determined genetically, genetic modification could be possible. Cultivars showed considerable variations in SCAS and SCAC. Significantly positive seasonal correlations of SCAS (Spearman's rank correlation coefficient, ρ = 0.518) and of SCAC (ρ = 0.681) were observed among recombinant inbred lines (RILs) derived from the cross between cultivars Nattoshoryu and Hyokeikuro 3. Quantitative trait locus (QTL) analyses and statistical analyses using generalized linear models showed that QTLs for SCAS (qSCAS1, qSCAS2, and qSCAS3) and SCAC (qSCAC1 and qSCAC2) were located on chromosomes 4 (linkage group [LG]: C1), 6 (LG: C2), and 8 (LG: A2) of the RILs. Interactions between QTLs were also observed. SCAS and SCAC are traits controlled by QTLs, which could provide significant insight into their causes and mechanisms. These undesirable characteristics could be improved through breeding.
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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".