Genetic diversity and selection of genotypes to enhance Zn and Fe content in common bean
Bibliographic record
Abstract
L.) is an important source of dietary protein and minerals worldwide. Genes conditioning variability for mineral contents are not clearly understood. Our ultimate goal is to identify genes conditioning genetic variation for Zn and Fe content. To establish mapping populations for this objective, we tested mineral content of 29 common bean genotypes. Chemical analyses revealed significant genetic variability for seed Zn and Fe contents among the genotypes. Genetic diversity was evaluated with 49 primer pairs, of which 23 were simple sequence repeats (SSR), 16 were developed from tentative consensus (TC) sequences, and 10 were generated from common bean NBS-LRR gene sequences. The discriminatory ability of molecular markers for identifying allelic variation among genotypes was estimated by polymorphism information content (PIC) and the genetic diversity was measured from genetic similarities between genotypes. Primers developed from NBS-LRR gene sequences were highly polymorphic in both PIC values and number of alleles (0.82 and 5.3), followed by SSRs (0.56 and 3.0), and markers developed from TC (0.39 and 2.0). genetic similarity values between genotypes ranged from 14.0 (JaloEEP558 and DOR364) to 91.4 (MIB152 and MIB465). Cluster analysis clearly discriminated the genotypes into Mesoamerican and Andean gene pools. Common bean genotypes were selected to include in crossing to enhance seed Zn and Fe content based on genetic diversity and seed mineral contents of the genotypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".