Research Status and Prospect of QTL Mapping for Tillers and Other Traits in Forage Sorghum
Bibliographic record
Abstract
Forage sorghum is an important raw material for winemaking and livestock feed. It has many excellent agronomic traits. Its genome-wide sequencing work has been completed. Molecular marker-assisted selection technology is widely used in sorghum breeding work. The number of genetic loci affecting the sorghum agronomic traits has been Be positioned. Tillering, as an important plant type trait, has an important influence on the physiological characteristics of sorghum, such as the tolerance, lodging resistance and light absorption efficiency. At present, considerable progress has been made in gene mapping of plant height, ear length, leaf morphology and other traits of forage sorghum. However, the progress of gene mapping for tillering is relatively slow due to the fact that tillering traits are easily affected by various factors. This study summarized the recent research progress of QTL mapping for tillering traits and other agronomic traits in sorghum, and proposed that some conserved sequences with high homology were retained during the evolution of different crop varieties, which proved that there was correlation between different traits of the same species. In sorghum breeding research, combining genetic engineering breeding with experimental statistics and quantitative genetics can better reveal the contribution of various factors to sorghum agronomic traits, improve breeding efficiency and reduce the loss of human and material resources.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".