Bibliographic record
Abstract
The gluten in wheat is the key to making staple foods like bread taste good. But it is also the main cause of celiac disease and some gluten intolerance diseases. Nowadays, there are many ways to reduce gluten, such as RNA interference, gene editing, mutagenesis screening and molecular marker breeding. Gene editing technologies like CRISPR/Cas9 can precisely knock out or modify multiple copies of the alcohol-soluble protein gene. This can significantly reduce the immune response while still retaining the processing performance of the dough. Researchers have obtained low-gluten wheat strains without genetically modified residues. Methods such as RNAi and TILLING can also lower the level of low-gluten protein and improve the nutritional components of wheat. These improvement measures not only offer celiac disease patients safer choices of staple foods, but also drive the development of healthy foods. In the future, if multi-omics analysis, personalized breeding and synthetic biology can be combined, it is possible to cultivate a new generation of low-gluten wheat that is both safe and delicious. This is precisely the goal of our research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".