Soybean seed lustre phenotype and surface protein cosegregate and map to linkage group E
Bibliographic record
Abstract
Soybean (Glycine max (L.) Merr.) seeds vary in their surface properties. The lustre, or glossiness, of seeds has been classified into several different phenotypes. Soybean seeds that have a dull lustre or moderate bloom (B) may also have abundant seed surface protein, namely, an abundance of the hydrophobic protein from soybean (HPS). The seed surface protein HPS is an allergen (Gly m 1) that causes asthma in persons allergic to soybean dust. In this study, seed lustre and surface protein content are compared among 71 different soybean cultivars and lines. Dull-seeded phenotypes usually possessed abundant surface protein in comparison to shiny-seeded types, although exceptions were observed. An F2 population of 82 individuals from a cross of OX281 (dull lustre, abundant HPS) and Mukden (shiny lustre, trace amounts of HPS) provided a basis for inheritance studies and genetic mapping analysis. Results indicate that dull seed lustre (B) and surface protein (Hps) loci are dominant Mendelian traits that cosegregate and map to soybean linkage group E. Molecular markers were used to construct a genetic map of 28 cM encompassing B and Hps. Two different molecular markers cosegregated with each of the loci. This study provides additional evidence that Hps may play a role in the adhesion of endocarp tissues to the seed, and offers new methods of selection for seed lustre and surface protein composition in soybean.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".