Development of a specific SCAR marker for the Ns genome of <i>Psathyrostachys huashanica</i> Keng
Bibliographic record
Abstract
Wang, J., Du, W., Wu, J., Chen, X., Liu, C., Zhao, J., Yang, Q. and Li, F. 2014. Development of a specific SCAR marker for the Ns genome of Psathyrostachys huashanica Keng. Can. J. Plant Sci. 94: 1441–1447. Psathyrostachys huashanica Keng (2n=2x=14, NsNs) possesses many agronomically desirable traits that could be used in wheat improvement. We have previously produced a complete set of wheat–P. huashanica disomic addition lines (1Ns–7Ns, 2n=44=22 II). To track the addition of P. huashanica chromatin in wheat rapidly and effectively, a repetitive sequence of 1665 base pairs, designated pHs8, was isolated based on 21 different Triticeae species, including the parents’ common wheat cv. 7182 and P. huashanica, by RAPD analysis. The diagnostic fragments of the RAPD marker OPF151665 were cloned, sequenced, and converted into a sequence-characterized amplified region (SCAR) marker, known as RHS12. Southern hybridization using labeled pHs8 as probe showed intense hybridization signals on P. huashanica, but not on the other 20 species at all. RHS12 was validated using 21 different plant species and a complete set of wheat–P. huashanica disomic addition lines. Our results indicated that the SCAR marker targeted the Ns genome of P. huashanica and it was present in all seven P. huashanica chromosomes. The newly developed SCAR marker should help wheat breeders to screen for genotypes containing P. huashanica chromatin with low costs and high throughput.
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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) | 0.001 | 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".