A genomic approach to nutritional, pharmacological and genetic issues of faba bean ( <i>Vicia faba</i> ): Prospects for genetic modifications
Bibliographic record
Abstract
Cultivated faba bean (Vicia faba) is widely used as human food, especially in Europe, Northern Africa and China. In view of its superior feeding value over field peas or other legumes, it is also widely used as animal feed for a variety of species. V. faba also contains medically important components such as 3,4-dihydroxyphenylalanine (levo-DOPA, L-DOPA), the principal treatment used for Parkinson's disease patients. However, this species also contains several antinutritional components, including the pyrimidine glycosides vicine and convicine; phytates; and the sucrose galactosides including raffinose, stachyose and verbascose. We have undertaken a genomic project to provide publicly available expressed sequence tag sequences (EST) prepared from early to mid developing embryo in an attempt to identify genes that are likely to be involved in the biosynthesis of L-DOPA and the vicine group of compounds. As initial examples of the utility of this approach, we describe the complete sequence of fabatin, new defensins, type 4 metallothioneins and a variety of other key genes which were identified in this EST library. No candidate sequences corresponding to the biosynthesis of L-DOPA or the vicine group could be identified at this early stage of seed development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".