Microsatellite isolation and characterization in sunflower (<i>Helianthus annuus</i>L.)
Bibliographic record
Abstract
Development of microsatellite markers for sunflower (Helianthus annuus L.) was performed to estimate their frequency, nature (structure), levels of polymorphism, usefulness for genotype identification, and calculation of genetic relationships between inbred lines representing the species diversity. Isolation was performed from a small-insert genomic library followed by hybridization screening using oligonucleotide probes containing different nucleotide arrays. In this work, 503 unique microsatellite clones were sequenced and 271 PCR primer sequences bordering the microsatellite repeat were designed. For polymorphism assessment, 16 H. annuus germplasm accessions were checked and 170 of the primers tested were shown to be polymorphic for the selected lines. The polymorphic microsatellites produced an average of 3.5 alleles/locus and an average polymorphism information content (PIC) of 0.55. The most frequently found motifs within polymorphic simple-sequence repeats (SSRs) were: (GA)n, (GT)n, (AT)n, followed by trinucleotides (ATT)n, (TGG)n, and (ATC)n, and the tetranucleotide (CATA)n. Most of the 170 SSRs obtained showed important differences in the 16 reference inbred lines used for their characterization. In this work, 20 of the most informative SSRs destined to sunflower genotyping and legal fingerprinting purposes are fully described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".