Proposal of International Gluten Research Group
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".