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Record W4386000399 · doi:10.1111/pbr.13138

Genetic dissection of endosperm hydration in malting barley (<scp><i>Hordeum vulgare</i></scp>)

2023· article· en· W4386000399 on OpenAlexaff
Joseph Jensen, Hannah M. Turner, Jennifer Lachowiec, Greg Lutgen, X. S. Yin, Jamie Sherman

Bibliographic record

VenuePlant Breeding · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsPQ Corporation (Canada)
FundersNational Institute of Food and AgricultureAmerican Malting Barley AssociationMontana Wheat and Barley CommitteeBrewers Association of Australia and New ZealandU.S. Department of Agriculture
KeywordsEndospermSteepingBiologyDormancyHordeumAgronomyHordeum vulgareHorticultureBotanyPoaceaeGerminationFood science

Abstract

fetched live from OpenAlex

Abstract Hydration of the endosperm is a critical part of the malting process that ensures proper modification of the grain. However, little is known about the genetic controls of endosperm hydration and its relationship to agronomic and malt quality traits. The extent of endosperm hydration is estimated through hydration index (HYI). We measured HYI, agronomic, and malt quality traits on a 169‐line subset of the NSGC Barley Core Panel, which includes global malt lines, some dating from the inception of European breeding programmes. Utilizing GWAS, 61 QTLs were identified for HYI, dormancy, agronomic, and malt quality traits. Of these, six were found to be related to HYI and were located on 1H, 2H, 3H, 6H, and 7H. We found HYI QTLs cosegregating with kernel size and hardness (1H and 3H), malting quality (2H and 6H), and dormancy (2H and 6H). These results indicate that endosperm hydration after steeping can be improved by selecting high HYI alleles on 2H, 6H, and 7H, positively impacting malting quality without negatively impacting kernel size or dormancy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.211
Teacher spread0.186 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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