SSR Mapping for Locus Conferring on the Triple-Spikelet Trait of the Tibetan Triple-spikelet Wheat (<i>Triticum aestivum</i> L. concv. <i>tripletum</i>)
Bibliographic record
Abstract
Tibetan triple-spikelet wheat is an unique common wheat landrace in Tibet region of China, which has special triple spikelet trait with supernumerary spikelets and florets. Molecular Mapping the control gene locus conferring on triple spikelet trait and mining the closely linked markers would be facilitate high-yield wheat breeding by marker-assisted selection approach. In this study, derived lines TTSW-5 from Tibetan triple spikelet wheat and common-spikelet wheat, Jian 3 and Chuanmai 55, were used to construct the F 2 populations for , phenotypic analysis and SSR genotyping. Genetic analysis of phenotypic traits showed that triple spikelet trait of Tibetan triple spikelet wheat are controlled by two independent recessive gene loci. One QTL linked to the triple spikelet trait was detected on the chromosome 2A by using F 2 population from TTSW-5/Jian 3 combination and SSR markers, the targeted locus was located within SSR markers, Xgwm275 and Xgwm122, the genetic distance between two markers is 6.6CM with LOD value 6.19, which can be explained 33.1% phenotypic variation, The detected locus tentatively named as qTS2A -1 . We speculated that qTS2A -1 locus might be one of dominant loci for controlling the triple spikelet trait , Therefore, SSR markers, Xgwm275 and Xgwm122, might be used as assisted selection markers for triple spikelet trait in high-yield 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 | 0.001 |
| 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) | 0.002 | 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".