Diversity and relationships of<i> Miscanthus sinensis</i> from 20 geographical distributions in China based on SSR markers
Bibliographic record
Abstract
Miscanthus is a C4 herbaceous perennial genus, and it was chosen as a bioenergy crop due to high biomass yield. Miscanthus sinensis has many phenotypes which are adapted to various environments in China. In this study, 421 accessions of M. sinensis were collected from 22 provinces, and the genetic polymorphisms amongst these germplasm collections were identified using 20 primer pairs designed against 10 each from expressed sequence tag-simple sequence repeats [EST-SSR (eSSR)] and genomic SSR (gSSR) transferable markers from barley. A total of 95 SSR polymorphic bands were detected producing a 100% polymorphic rate among these M. sinensis accessions. The gSSR markers showed a richer genetic polymorphism than eSSR markers. Based on the unweighted pair group method with arithmetic mean (UPGMA) clustering, there was a distinct sub-population separation in M. sinensis, which indicates that geographical differences and natural selection are the driving forces for genetic variation and evolution in the species. The 20 pairs of barley markers matched to 26 polymorphic bands associated with date of heading, plant height, leaf weight, stem weight, leaf/stem ratio, and total biomass yield. Eleven marker polymorphic bands were associated with the date of heading, 4 with plant height, 10 with leaf weight, 7 with stem weight, 3 with leaf/stem ratio, and 10 with biomass yield.
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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 | 0.002 | 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".