Marker-Assisted Selection in Cassava: From Theory to Practice
Bibliographic record
Abstract
Cassava ( Manihot esculenta Crantz) is an important food crop in tropical and subtropical regions of the world, with high starch content and significant industrial application value. Marker-assisted selection (MAS) is an important technology in plant breeding, especially in cassava improvement, showing great potential for application. Mas provides an efficient genetic improvement method for cassava breeding, which can accelerate the improvement process of traits, especially in key agronomic traits such as starch content and disease resistance. This study systematically reviews the relevant theories of MAS, discusses the discovery of cassava genetic markers, and highlights the role of MAS in improving the efficiency and accuracy of selection, especially in accelerating the development of new cassava varieties through case studies. Despite the technical and resource challenges, the application prospect of MAS technology in cassava breeding remains optimistic. This review aims to provide in-depth scientific reference and practical guidance for cassava breeders and researchers worldwide, and to provide directions for future research.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".