Establishment of an effective set of simple sequence repeat markers for sunflower variety identification and diversity assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to identify an efficient set of simple sequence repeat (SSR) markers for sunflower (Helianthus annuus L.) variety fingerprinting, relying on semi-automated analysis conditions. Based on criteria such as quality of amplification products, co-dominant and single locus, 78 SSR markers were selected and used to assess the genetic variability among a large set of 124 sunflower inbred lines, including 67 female maintainers (M lines) and 57 male restorers (R lines). They revealed a total of 276 alleles across the 124 elite inbred lines, with a mean of 3.5 alleles per SSR locus. The polymorphism index content per locus varied from 0.06 to 0.81, with an average of 0.51. Relationships among the inbred lines were studied using estimations of Rogers' distances. The great majority of the distance estimates ranged between 0.4 and 0.6, but distances between some pairs of lines were less than 0.1. The genetic diversity value was similar within each subset of R and M lines and low, but significant differentiation was found (G ST = 0.049) between the two pools. The selected set of SSRs proved to be useful both for sunflower fingerprinting and genetic diversity assessment.Key words: genetic diversity, genotyping, Helianthus annuus, multiplex PCR, simple sequence repeats (SSR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it