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Record W4385705618 · doi:10.26898/0370-8799-2023-6-3

Study of the parent material of soft winter wheat for breeding for grain quality

2023· article· en· W4385705618 on OpenAlexaboutno aff
Н. С. Кравченко, S. V. Podgorny, N. G. Ignatieva, V. L. Chernova

Bibliographic record

VenueSiberian Herald of Agricultural Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsGlutenAgronomyTraitBiologyBiotechnologyGeographyFood science

Abstract

fetched live from OpenAlex

The sources of valuable traits for targeted use in the breeding of new winter wheat varieties with improved quality traits are presented. The results of the evaluation of the parent material on the expression of economically valuable features in the collection samples of soft winter wheat are presented. The paper presents data on 31 varieties of different ecological and geographical origin. Field experiments were conducted in 2018-2020 in the breeding rotation under the conditions of the southern zone of the Rostov region. Quality indicators of the varieties: thousand grain weight, protein content, quantity and quality of gluten in the grain, grain unit, total vitreousness, baking properties were determined by standard methods and GOSTs. As a result of the clustering of varieties, it is shown that the breeding program to create adaptive varieties with high grain quality should include as basic parent material the varieties which are included in the 5th and 6th clusters L-19578 (Russia), Etana (Germany), Warwick (Canada), Akter (Germany), MV-15-09 (Hungary), Simonida (Serbia), GK Hollo (Hungary), Webster (Canada), Wisdom (Canada), No. 42 CIMMYT (USA), and KS 96 WGRC 37 (USA). These varieties showed good results in the southern zone of the Rostov region. The other varieties of the collection nursery are recommended to be included in the breeding work in accordance with the principle of complementarity, as mutually complementary varieties in the expression of a particular trait or property.

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)0.001
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 score0.742
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.059
GPT teacher head0.290
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2023
Admission routes1
Has abstractyes

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