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Скринінг генофонду сої культурної за стійкістю до біо– та абіотичних чинників

2017· article· en· W2724759273 on OpenAlexaboutno aff
С. С. Рябуха, О. О. Посилаєва, Т. В. Сокол, П. В. Чернишенко

Bibliographic record

VenuePlant Breeding and Seed Production · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Biological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFusariumBiologyResistance (ecology)HorticultureAdaptabilityAgronomyBiotechnologyEcology

Abstract

fetched live from OpenAlex

The results of studies of collection and breeding soybean material for resistance to Fusarium, drought and heat are presented. The aim and tasks of the study. Search for soybean starting material with high adaptive features and resistance to Fusarium, formation of source collections, their use in breeding to develop high-yielding varieties that are resistant to Fusarium, drought and heat. Materials and methods. 300 accessions were evaluated for resistance to Fusarium on infectious background by standard methods in 2005-2012. Adaptability of the modern soybean assortment (83 accessions) to heat and drought was evaluated under contrasting conditions in the field (control) and in a manmade rainfall shelter in 2012-2013. Results and discussion. A working soybean collection consisting of 51 accessions from 11 countries was formed by individual resistance to Fusarium. Accessions OAC Shire (513 g/m2), Sofia (475 g/m2), Lara (448 g/m2), Predator (440 g/m2), Sviatohor (433 g/m2), Sharm (428 g/m2), T1 (425 g/m2), and MN 1401 (410 g/m2) combine resistance to Fusarium with high yield capacity. Another working soybean collection consisting of 83 accessions from 15 countries was formed by resistance to drought and heat. Variety Hali showed a very high drought resistance; accessions Soniachna, Soier 345, Pripiat were highly resistant; accessions F 50 R/W, Yankan, Antrathyt, Alisa, Tanais, Samer 2, Baika, Labrador, Emerson, L 101, Larisa , L 55-13, Gaillard, Bilosnizhka, Sprytna, Hera, Merlin, L 52-13, Karikachi, Donskaya (milky), Sprint, N 0300, Walsh, UIR 021752, and Desna were medium resistant. Variety Hali combines a very high drought resistance with high productivity; accession Soier 345 - high drought resistance with high productivity; accessions F 50 R/W, Yankan, Baika, L 101, Larisa, Gaillard, Sprytna, Donskaya (milky), UIR 021752, and Desna - medium drought resistance with high productivity. Conclusions. The working soybean collection by individual resistance to Fusarium includes 51 accessions from 11 countries. The working soybean collection by resistance to drought and heat includes 83 accessions from 15 countries

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.209
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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