Electrophoretic patterns of gliadin as markers of genotypes in the analysis of the durum wheat landrace Kubanka
Bibliographic record
Abstract
Background. The collection of durum wheat landraces (Triticum durum Desf.) at VIR contains unique material of the “Russian northern branch”, which is not found in any other collections worldwide. Studying the genetic diversity of such wheat accessions according to their gliadin bands as markers of genotypes is important for identification and conservation of their gene pool authenticity. Materials and methods. For the first time, molecular markers were used to differentiate among 38 accessions of the local durum wheat variety known under the name of “Kubanka”, collected and placed into the VIR collection in the 1910–1940s, and five accessions from the seed genebanks of the USA and Canada. Electrophoretic patterns of gliadin were used as markers of genotypes within the polymorphic cultivar. Recording bands in the form of “protein formulas” allows the researcher to evaluate the polymorphism of each accession and the diversity within the collection. Gliadin analysis was performed on single grains according to the standard method adopted at VIR and approved by ISTA. Results and conclusions. Fourteen major biotypes marked with gliadin bands were identified. Depending on the component composition of the α-zone encoded by alleles of the GLI-2A locus, biotypes were combined into 4 groups. Within the groups, biotypes are determined by alleles of the GLI-1A, GLI-1B, GLI2B loci. Genetically close monotypic accessions and polytypic ones incorporating 2 to 6 biotypes were identified. Group I is typical for the European part of Russia as well as for Kazakhstan and Kyrgyzstan. Accessions of this group can be attributed to the Volga steppe ecotype. Group II biotypes are widespread in Altai Territory, Orenburg and Astrakhan Provinces of Russia; Group III in Stavropol Territory, Russia, and Kyrgyzstan; Group IV only in Altai Territory. The greatest genetic diversity was exhibited by the ‘Kubanka’ accessions from Altai and Krasnodar Territories, and Kyrgyzstan.
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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 | 0.000 |
| 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) | 0.001 | 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".