Bibliographic record
Abstract
The grain-filling period is a crucial stage that determines the yield and quality formation of wheat grains, involving a complex spatio-temporal regulation process. To gain a deeper understanding of the dynamic changes in gene expression and their molecular mechanisms during grain development, this study, based on multiple time points and multiple tissues, constructed a spatio-temporal transcriptome map of wheat grains covering the entire grain-filling period. Three key periods - early, middle, and late grain-filling - were selected, and the main tissues such as endosperm, embryo, and aleurone layer were collected respectively. High-quality transcriptome data were obtained by using high-throughput RNA sequencing technology. Through differential expression analysis, time series clustering, tissue-specific expression analysis and functional annotation, a large number of key genes and transcription factors related to carbon and nitrogen metabolism, starch synthesis, hormone regulation, stress resistance response, etc. were identified. This study also constructed multiple regulatory networks closely related to grain development and revealed the potential roles of non-coding Rnas and epigenetic factors in regulation, providing resource support for the analysis of the complex regulatory networks of grain development. This research will lay a solid foundation for the improvement of wheat quality, molecular breeding and the functional study of key regulatory genes during the grain-filling period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".