Genetic diversity in soybean genotypes from north‐eastern China and identification of candidate markers associated with maturity rating
Bibliographic record
Abstract
Abstract Random amplified polymorphic DNA (RAPD) and simple sequence repeat (SSR) markers were used to estimate the genetic relationships among 101 soybean cultivars developed in north‐eastern China. Fifty‐three fragments of the 100 RAPD markers and 35 SSR markers tested were polymorphic across the 101 soybean cultivars. Similarity values among these soybean cultivars ranged from 45.2% to 100% for RAPD data, and ranged from 36.1% to 100% for SSR data. The similarity matrices for SSR data and RAPD data were moderately correlated ( r = 0.31, P < 0.05). Cluster analyses indicated that the cultivars released from the same seed company were mostly grouped together. A principal component analysis, based on the combined RAPD and SSR data, yielded a good separation of soybean varieties with different maturity ratings [represented by soybean Heat Unit (HU)]. The varieties with HU < 2200 were well separated from those with HU > 2200. Four RAPD markers and eight SSR markers were significantly associated with the maturity ratings of soybean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".