USE IN SELECTION OF BLACK CURRANTS OF GENOFOND OF KOKINO BASE STATION OF ALL - RUSSIAN SELECTION AND TECHNOLOGICAL INSTITUTE OF HORTICULTURE AND BREEING NURSERY
Bibliographic record
Abstract
UDC 634.723.1:631.526.52 USE IN SELECTION OF BLACK CURRANTS OF GENOFOND OF KOKINO BASE STATION OF ALL-RUSSIAN SELECTION AND TECHNOLOGICAL INSTITUTE OF HORTICULTURE AND BREEING NURSERY Sazonov F.F., Candidate of Agricultural Sciences All-Russian Selection and Technological Institute of Horticulture and Breeding Nursery Bryansk Region, Russia Danshina O.V., Post-graduate student Bryansk State Agricultural Academy, Bryansk, Russia Phone: +7 (920) 607-01-73, Email: sazon-f@yandex.ru ABSTRACT The genetic sources of the most valuable qualities for selective usage including good crop capacity, high stability against pathogenesis, winter stableness, big fruitfulness, the quality of fruit and other special directions of selection have been determined from the collection of black currants of the Kokino Base station of All-Russian Selection and Technological Institute of Horticulture and Breeding Nursery. KEY WORDS Black currant; Sort; Variety; Selection. Academician N. I. Vavilov was one of the first who set a problem of collecting, preservation and study of the genofond of cultivated plants and their wild relatives through natural selection in the environmental conditions of plant forms with the desired traits for selection [2]. Formation of the genetic collection of black currants of Kokino All-Russian Selection and Technological Institute of Horticulture and Breeding Nursery (ASTIHBN) is based on the collection of varieties and forms of domestic and foreign selection that are highly resistant to unfavorable climatic factors non-chernozem zone, resistance to biotic stresses, that have excellent fruit quality, conservation varieties created at the institute. The main principle for the replenishment of the currant is the availability of accessions of economically useful traits (one or more) for the conduct of selection to further assortment improvement. Beginning in the 1970s, except for the quantitative growth assortment, sequential enrichment of genetic diversity in the original forms was developing. Russian breeders have valuable complex donors with high levels of economic and important features. These donors include genoplazm of 4-6 currant varieties. In addition to black currant European and Siberian subspecies, ecotypes and currants Scandinavian spruce grouse in breeding programs are widely used currants offspring: moss (R. procumbes Pall.), few-flowered (R. pauciflorum Turcz.), key (R. fontaneum Boczkarn.), ussuri (R. ussuriensis Jancz.), pedunculate (R. retiolare Dougl.), Canada (R. canadensis Jancz.), bracteolate (R. bracteosum Dougl.) and adhesive (R. glutinosum Benth.). Selective work is based on the methods of cross-species distant hybridization, converged crosses, backcrossing, sib mating and inbreeding. Involving in the selection work such genetically varied and geographically distant source made it possible to get breeding material on a large scale of variability and rich inheritance. They bred new varieties with high levels of agronomic traits [5]. MATERIALS AND RESEARCH METHODS Material research included a number of black currant varieties and forms of interspecific origin, as well as its offspring. In general, they are derivatives of European, Scandinavian and Siberian sub-species of Siberian grouse and adhesive current (R.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".