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The heterosis value of economically valuable traits in the multi-row hybrids F1 of spring barley obtained by saturating crossings

2021· article· en· W3133870445 on OpenAlex
N. A. Kryuchkova, G. А. Murugova, А. Г. Клыков

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
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Bibliographic record

VenueGrain Economy of Russia · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsHeterosisHybridDominance (genetics)BackcrossingBiologyAgronomyGeneGenetics

Abstract

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The current paper has presented the analysis results of the value of heterosis and the degree of phenotypic dominance of hybrids F 1 obtained by saturating crossings of two-row and multi-row spring barley forms. The purpose of the study was to determine the value of heterosis and the degree of phenotypic dominance of the main quantitative traits of the multi-row hybrids of spring barley. The study was carried out in the laboratory for grain crops and groats breeding of the FSBSI FRC of Agrobiotechnologies of the Far East named after A. K. Chayka. There were studied 68 varieties of multi-row spring barley forms selected from the world collection of IPI of various ecological and geographical origin. The two-row varieties ‘Primorsky 98’, ‘Primorsky 44’, ‘Primorsky 89’, ‘Tikhookeansky’ and ‘Vostochny’ developed by FSBSI FRC of Agrobiotechnologies of the Far East named after A. K. Chayka were taken as maternal forms. Four multi-row barley varieties ‘Kazminsky’ (Khabarovsk Territory), ‘Peguis’ (Canada), ‘Kolchan’ (Altai Territory), ‘07N1’ (China) with valuable economic traits were taken as a paternal form. There was conducted a five-fold backcrossing of hybrids with paternal forms and there were selected the populations of multi-row genotypes. The most of the hybrids F 1 manifested their heterosis simultaneously according to four traits, productive tillering, number of grains per main head, grain weight per main head, and grain weight per a plant. Only two hybrids ‘Primorsky 98 x Kolchan’ and ‘Primorsky 89 x Peguis’ showed a depression. There was identified heterosis by the traits ‘number of grains per main head’, ‘grain weight per main head’ in all hybrids. When analyzing the inheritance of the trait ‘grain weight per a plant’, there was established overdominance of this trait at the highest values of the hybrids ‘Tikhookeansky x Peguis’ (9.7) and ‘Primorsky 44 x 07N1’ (5.2), the heterosis degree was 50.4% and 82.2%, respectively. There has been established that three hybrids ‘Primorsky 98 x 07N1’, ‘Primorsky 44 x 07N1’, ‘Tikhookeansky x Peguis’ can belong to the most promising ones.

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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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.201
Teacher spread0.189 · 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