Female Spike Phenotypic Statistics of Teosinte Introgression and Development of SNP Marker
Bibliographic record
Abstract
Two introgressive groups of zheng 58-teosinte and B 73-teosinte were exploited by the method that one generation of hybrid and three generations of backcross and five generations of selfing with maize as receptors and teosinte as chromosome fragment donor. Then investigation on phenotype of the ears of each line was carried on. Results showes that in B 73-teosinte introgressive group,ear length of 83% family on 9- 14 cm,ear rows of 85% on 12- 16; the line grain number of 58% on 17- 23,grain length of 92. 5% family was concentrated in the 0. 7- 1. 0 cm,diameter of shaft of 73. 5% on 1. 8- 2. 4 cm,hundred grain weight of 34. 5% concentrated in 18- 23 grams,43. 5% concentrated in 28 and 33 grams. In zheng 58-teosinte introgressive group,ear length of 83. 5% is focused on the 10- 15 cm; ear rows of 47. 5% is 10 lines,the line grain number of 83% on 14- 25,grain length of 70. 5% concentrated in the 0. 8- 0. 9 cm,diameter of shaft of 84. 5% concentrated in the 1. 8- 2. 4 cm,hundred grain weight of 74. 5% concentrated in 28- 33 g. We develop 211 polymorphism molecular markers between zheng 58 and teosinte by sequencing,including 199 SNPs marker and 12 INDEL marker. This study not only supplies new germplasm for maize but also provides moleculars markers for next gene clone,accelerating the progress of maize 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 | 0.000 | 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 | 0.001 |
| 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".