Genetic Control of Beta-carotene, Iron and Zinc Content in Sweetpotato
Bibliographic record
Abstract
Micronutrients deficiency is a major contributor to poor health in developing countries. It can be alleviated by biofortification or enrichment of staple crops with micronutrients. Sweetpotato is a major staple crop in numerous tropical countries and is naturally biofortified. In spite of extensive promotion of orange-fleshed sweetpotatovarieties (OFSPs), they are poorly utilized as staple food in most parts of West Africa because of their low dry matter and high sugar content. Beta-carotene is positively correlated with iron and zinc content in sweetpotato. Development of sweetpotato cultivars with end-user preferred traits and higher content of beta-carotene, iron and zinc will alleviate their deficiencies. Knowledge on the genetic control of these traits is critical for their improvement in sweetpotato. This study used diallel mating design to estimate general combining ability (GCA) and specific combining ability (SCA) of storage root beta-carotene, iron and zinc content to determine the genetic control of these traits for sweetpotato breeding. A general model for estimating genetic effect, Gardner and Eberhart analysis II (GEAN II), was used for data analysis. Genetic variability for the traits indicated that they were mostly controlled by additive gene effect. Significant heterosis was found indicating that levels of these micronutrients can be improved in sweetpotato through breeding.
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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 | 0.000 |
| 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".