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Record W2327469690 · doi:10.4238/2015.march.20.7

Characterization of agronomic and quality traits and HSW-G5 compositions from the progenies of common wheat (Triticum aestivum L.) with different protein content

2015· article· en· W2327469690 on OpenAlexaff
Miao Bian, Dongqing Sun, Dao-Jie SUN, Genlou Sun

Bibliographic record

VenueGenetics and Molecular Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsSaint Mary's University
FundersNational Natural Science Foundation of China
KeywordsGluteninCultivarBiologyMendelian inheritanceCommon wheatComposition (language)AgronomyHorticultureGeneGeneticsProtein subunitChromosome

Abstract

fetched live from OpenAlex

High molecular weight glutenin subunits (HMW-GS) play an essential role in wheat processing quality. In this study, we evaluated the genetic pattern with HMW-GS composition between generations and examined whether agronomic and quality traits were correlated with each other. A wheat (Triticum aestivum L.) cultivar with high protein content and 2 cultivars with low protein content were subjected to a reciprocal cross. Sixteen agronomic and 4 quality characteristics were investigated. A total of 216 seeds from each F2 generation were chosen randomly and analyzed for HMW-GS composition using sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Agronomic and quality characteristics were not significantly different between reciprocal crosses, indicating no cytoplasmic effect on the characteristics studied. The separation ratio of 2 HMW-GS loci was 9:3:3:1, indicating no linkage between any 2 loci. The novel HMW-GS N was detected in cultivar R145, which did not follow the Mendelian segregation ratio. A Glu-A1a(1) band was not detected in 1 individual from Tian8901xR145. Average grain weight per spike was significantly correlated with quality characteristics and may be a suitable criterion for selecting high protein content in wheat breeding programs.

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 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 score0.549
Threshold uncertainty score0.139

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 studies0.0000.000
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 head0.123
GPT teacher head0.301
Teacher spread0.178 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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