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Record W2292815831 · doi:10.1007/978-4-431-55675-6_31

Proposal of International Gluten Research Group

2015· book-chapter· en· W2292815831 on OpenAlexaff
Tatsuya M. Ikeda, William J. Rogers, G. Branlard, Roberto J. Peña, S.E. Lerner, Adriana C. Arrigoni, Wujun Ma, R. Appels, O. M. Lukow, William J. Hurkman, Marie J. Appelbee, Mike Sissons, J. M. Carrillo, Zhonghu He

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGluteninGlutenContext (archaeology)CultivarAlleleIdentification (biology)GliadinBiotechnologyBiologyGeneGeneticsAgronomyFood scienceBotany

Abstract

fetched live from OpenAlex

In a scenario of climate change and rapidly rising urban populations demanding processed foods, it is necessary to develop new wheat cultivars combining high yield potential, disease resistance, and stability for yield and processing quality, even under heat or drought stress conditions. Allelic variation for gluten proteins (glutenin subunits and gliadins) is a major determinant of differences in dough viscoelastic properties observed between cultivars of both bread wheat and durum wheat. Technical difficulties in allelic identification due to the complexity of the protein profile produced by each cultivar and the use of different nomenclature systems in different laboratories has historically interfered with information exchange between research groups, a situation exacerbated by the vast number of possible profiles found in different cultivars due to the multi-allelic nature of the principal loci encoding gluten proteins ( Glu-1 , Glu-2, Glu-3 , Gli-1 and Gli-2 ). For the Glu-3 alleles, we have collaborated to unify criteria across laboratories and to compare four different methods of allelic identification (SDS-PAGE, 2-DE, MALDI-TOF-MS and PCR), and have shown that the four methods can be regarded as complementary techniques for allelic identification. We seek to continue addressing remaining analytical challenges, place the findings in the context of the Catalogue of Gene Symbols for Wheat, and, with unified criteria, initiate work to define better the relationship between specific gluten proteins and processing quality attributes. Therefore, we propose a new system to share materials through public gene banks in collaboration with the Catalogue, and the formation of a wider international group aimed at facilitating the resolution of the remaining problems in the field. We also propose to extend our collaboration by forming a wheat quality expert working group under the Wheat Initiative. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0030.006
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0340.018

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 head0.145
GPT teacher head0.422
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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
Published2015
Admission routes1
Has abstractyes

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