Distribution of genes related to plant height, kernel weight and disease resistance among wheat cultivars from major countries
Bibliographic record
Abstract
Using the molecular markers for dwarfing genes(Rht-B1b and Rht-D1b)and kernel weight related genes(TaCwi-A1a and Hap-6A-A) and Lr34/Yr18/Pm38,the distributions of these genes were detected among 745 cultivars from 21 major wheat-growing countries.The results indicated that:(1) The frequencies of Rht-B1b and Rht-D1b in 745 cultivar were 42.1% and 28.7%,respectively,varying much in different wheat-growing countries.Generally,cultivars from the same country usually carried only one of Rht-B1b and Rht-D1b,frequencies only from Italy and Australia were higher for both Rht-B1b and Rht-D1b,which in high latitude areas such as Canada and Russia were lower due to less requirement for plant height.(2) TaCwi-A1a allele was widely distributed in 21 countries with a total frequency of 78.4%,and cultivars from different countries possessed all higher frequency of TaCwi-A1a except for Japan(50.0%),Germany(45.3%) and Chile(48.8%).The 29.3% of cultivars carried Hap-6A-A allele at TaGW2-6A locus,mainly distributing in spring and weak winter wheat,whereas Hap-6A-G was mainly present in winter and strong winter cultivars.(3) The 22.1% of cultivars had Lr34/Yr18/Pm38 allele,with higher frequency in USA(18.5%),Ukraine(28.6%),Russia(26.1%),Iran(20.0%),Turkey(34.8%),Hungary(50.0%),Bulgaria(38.9%),Romania(87.0%),Japan(80.0%),Canada(34.6%) and Australia(44.6%).(4)The molecular markers CWI 21 and CWI 22 for TaCwi-A1 can well differentiate TaCwi-A1a and TaCwi-A1b alleles,while the CAPS marker of TaGW2-6A can also be used for kernel weight selection due to its ability of discriminating Hap-6A-A and Hap-6A-G with great accuracy and repeatability.The information is very crucial for use of exotic germplasm in Chinese wheat breeding program.
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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 | 0.001 |
| 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".