Allelic diversity of simple sequence repeats among elite inbred lines of cultivated sunflower
Bibliographic record
Abstract
Simple sequence repeat (SSR) markers were developed for cultivated sunflower (Helianthus annuus L.) from the DNA sequences of 970 clones isolated from genomic DNA libraries enriched for (CA)n,, (CT)n, (CAA)n, (CATA)n, or (GATA)n. The clones harbored 632 SSRs, of which 259 were unique. SSR markers were developed for 130 unique SSRs by designing and testing primers for 171 unique SSRs. Of the total, 74 SSR markers were polymorphic when screened for length polymorphisms among 16 elite inbred lines. The mean number of alleles per locus was 3.7 for dinucleotide, 3.6 for trinucleotide, and 9.5 for tetranucleotide repeats and the mean polymorphic information content (PIC) scores were 0.53 for dinucleotide, 0.53 for trinucleotide, and 0.83 for tetranucleotide repeats. Cluster analyses uncovered patterns of genetic diversity concordant with patterns produced by RFLP fingerprinting. SSRs were found to be slightly more polymorphic than RFLPs. Several individual SSRs were significantly more polymorphic than RFLP and other DNA markers in sunflower (20% of the polymorphic SSR markers had PIC scores ranging from 0.70 to 0.93). The newly developed SSRs greatly increase the supply of sequence-based DNA markers for DNA fingerprinting, genetic mapping, and molecular breeding in sunflower; however, several hundred additional SSR markers are needed to routinely construct complete genetic maps and saturate the genome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".