USE IN SELECTION OF BLACK CURRANTS OF GENOFOND OF KOKINO BASE STATION OF ALL - RUSSIAN SELECTION AND TECHNOLOGICAL INSTITUTE OF HORTICULTURE AND BREEING NURSERY
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».