Sugarcane genetics: Underlying theory and practical application
Bibliographic record
Abstract
Sugarcane is recognized as the fifth largest crop globally, supplying 80% of sugar and 40% of bioenergy production. However, sugarcane genetic research has significantly lagged behind other crops due to its complex genetic background, high ploidy (8–13×), aneuploidy , limited flowering , and a long growth cycle (more than one year). Cross breeding began in 1887 following the discovery that sugarcane seeds could germinate. Both self- and cross-pollination and selection were conducted by sugarcane breeders, but new cultivars were often eliminated due to disease susceptibility. Within the Saccharum genus, different species possess variable numbers of chromosomes. Wild sugarcane species intercrossed with each other, leading to development of the ‘Nobilization’ breeding strategy, which significantly improved yield, sucrose , fiber content, and disease resistance , and accelerated genetic improvement of cultivars. In recent years, scientific achievements have also been made in sugarcane genome sequencing , molecular marker development, genetic linkage map construction, localization of quantitative trait locus (QTL), and trait-associated gene identification. This review focuses on the progress in sugarcane genetic research, analyzes the technical difficulties faced, presents opportunities and challenges, and provides guidance and references for future sugarcane genetics research and cultivar breeding. Finally, it offers directions for future on sugarcane genetics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".