GENETIC DIVERSITY OF WHEAT CULTIVARS ESTIMATED BY SSR MARKERS
Bibliographic record
Abstract
Presence and utilization of the genetic variability in the breeding programmes is prerequisite for their successfulness. Important factor for crop improvement is knowledge about the genetic diversity which providing a basis for the precise selection of parental combinations. Since beginning of 20th century, generation of wheat breeders and scientists in Croatia developed numerous advanced and successful wheat cultivars. Previous researches aimed to genetic diversity evaluation in Croatia were conducted by means of morphological traits, pedigree data (coefficients of parentage), proteins (glutenins and gliadins) and RAPD DNA markers. DNA markers detect directly variation of DNA sequence for particular loci and they are not under influence of environment, epistatic and pleiotropic effects. Microsatellite markers (Simple Sequence Repeats; SSRs), as highly polymorphic, informative and codominant DNA marker system, have been extensively used for genetic diversity studies on wheat world wide. A set of 98 wheat cultivars released in Croatia during the period 1905-2007, and 24 foreign cultivar (included because of their ancestral significance or as standards), were screened by 45 microsatellite markers, covering all three wheat genomes. The objectives of this study were to evaluate the microsatellites-based genetic diversity with emphasize on cultivars created at the Agricultural Institute Osijek, as well as to investigate SSR application for selection of genetically the most distant parental pairs. Preliminary data obtained by means of SSR markers showed a satisfactory level of genetic diversity and usefulness of microsatellites for parental selection.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".