A multi-parent faba bean ( <i>Vicia faba</i> L.) population for future genomic studies
Bibliographic record
Abstract
Abstract Faba bean ( Vicia faba L.) is a valuable grain legume and a staple protein crop in many countries. Its large and complex genome requires novel approaches for its genetic dissection. Here we introduce a multi-parent population developed from four founders (ILB 938/2, Disco/2, IG 114476 and IG 132238). The selection of parental lines was based on geographic (Colombia, France, Bangladesh and China), genetic and phenotypic diversity. The parental lines were inbred and then genotyped using 875 single nucleotide polymorphism (SNP) markers. Based on molecular data, the parents had high homozygosity and high genetic distance among them. The population segregates for several important traits such as seed morphology, seed chemistry, phenology, plant architecture, drought response, yield and its components, and resistance to Botrytis fabae. The population was checked for unbiased segregation in each generation by observing simply inherited Mendelian traits such as stipule spot pigmentation (SSP) and flower colour at different generations. All 1200 four-way cross F1 plants had pigmented flowers and stipule spots. The segregation ratios for white flower colour (single gene, zt2 ) fit 7:1, 13:3 and 25:7 at F2, F3 and F4 generations, respectively, and the segregation ratio of SSP (two recessive unlinked genes, ssp1 and ssp2 ) fit 49:15 and 169:87 at the F2 and F3 generations, respectively, demonstrating unbiased generation advance. We will subject the F5 generation of this population to a high-throughput SNP array and make it available for further phenotyping and genotyping.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".