Bibliographic record
Abstract
Faba bean (Vicia faba) is an ancient legume species known for its high protein content. The usage and consumption of the faba bean is limited by a glycoside, vicine-convicine (VC). Consumption of VC causes haemolytic anemia in individuals with the genetic condition called favism. Faba beans with low VC concentration are opening the possibility of reduction of favism disease, but there are many challenges in analyzing VC concentration. The objective of this study was to develop expressed sequence tag (EST) markers that can differentiate between low VC content (LVC) and high VC content (HVC) faba bean genotypes. Three single nucleotide polymorphisms (SNPs) were discovered that distinguished between LVC and HVC genotypes. The SNPs were validated using Kompetitive Allele Specific PCR (KASP) and mass spectrometry phenotyping. Molecular marker SNP 316 (Intron of Medtr2g009270 at 1,851,012 bp) was the most successful marker in differentiating between LVC, HVC, and heterozygous faba bean genotypes. This marker has applications in seed selection and acceleration of breeding programs, which is the first step towards allowing all consumers concerned with the effects of favism to enjoy the nutritional value of faba bean.
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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 | 0.001 |
| 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".