Sample preservation and plant sex prediction in white guinea yam (Dioscorea rotundata Poir.)
Bibliographic record
Abstract
Introduction: Yam (Dioscorea spp.) is an economically important staple food in tropical regions, especially for people in West Africa. Understanding of the flowering behavior of the crop to determine potential manipulation available to accomplish crop improvement at early stage remain key challenge in the yam breeding. The methods that reliably yield quality DNA and distinguishing sex type at the early stage of growth have been a challenge in yam genetics and breeding studies. This study assessed the effect of sample preservation methods on DNA quantity and quality during extraction and potential of DNA marker to diagnose plant sex at the early seedling stage in white Guinea yam. Materials and Methods: Five sample preservation methods were assessed for quality DNA extraction during field leaf tissue collection, namely liquid nitrogen, dry ice, silica gel, 95% ethanol, and oven drying. The predicted sex at the seedling stage using the molecular marker was further validated with the visual score for the sex phenotype at the flowering stage. Results: According to the findings of the present study, the DNA extracted from leaf samples preserved in liquid nitrogen, silica gel, dry ice, and oven drying methods were higher in molecular weights than samples stored in ethanol solution. Yam plant sex diagnosis with the DNA marker (sp16) identified a higher proportion of ZW genotypes (female or monoecious phenotypes) than the ZZ genotypes (male phenotypes) in the studied materials with 74% prediction accuracy. Conclusions: The results from this study provided valuable insights on suitable sample preservation methods for quality DNA extraction and the potential of DNA marker sp16 to predict sex in white Guinea yam.
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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 | 0.001 |
| 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 | 0.000 |
| 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".