Multi-locus genotype-based modeling reveals that rising temperatures can stabilize the flowering date of winter wheat
Bibliographic record
Abstract
Abstract Flowering-date stability is crucial for global adaptability of wheat to sustain a high grain yield within broad temperature ranges; however, the response mechanism of such stability to climate change remains unclear. Here, we developed a multi-locus genotype based (MLG-based) ecophysiological model to predict wheat-flowering date that allowed for the linkage of key photoperiod (Ppd) and vernalization (Vrn) genes to wheat flowering. The MLG-based model was then applied to reveal the responses of wheat-flowering-date stability to different allelic combinations under projected climatic conditions across the Northern China winter wheat Region. The results showed that the stability of the flowering date for wheat could be enhanced under projected RCP4.5 and RCP8.5 global warming scenarios with allelic combinations of major Vrn and Ppd genes. Our findings highlight the potential of introducing allelic combinations of the winter allele vrn-D1 and photoperiod-insensitive genes (Ppd-D1a) into currently-cultivated varieties in order to maintain a more stable flowering date, especially under future climate conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".