A stepwise route to domesticate rice by controlling seed shattering and panicle shape
Bibliographic record
Abstract
Asian rice ( Oryza sativa L.) is consumed by more than half of the world's population. Despite its global importance, the process of early rice domestication remains unclear. During domestication, wild rice ( Oryza rufipogon Griff.) acquired non-seed-shattering behavior, allowing humans to increase grain yield. Previous studies argued that a reduction in seed shattering triggered by the sh4 mutation led to increased yield during rice domestication, but our experiments using wild introgression lines show that the domesticated sh4 allele alone is insufficient for shattering loss in O . rufipogon . The interruption of abscission layer formation requires both sh4 and qSH3 mutations, demonstrating that the selection of shattering loss in wild rice was not as simple as previously suggested. Here we identified a causal single-nucleotide polymorphism at qSH3 within the seed-shattering gene OsSh1 , which is conserved in indica and japonica subspecies but absent in the circum -aus group of rice. Through harvest experiments, we further demonstrated that seed shattering alone did not significantly impact yield; rather, yield increases were observed with closed panicle formation controlled by SPR3 and further augmented by nonshattering, conferred by integration of sh4 and qSH3 alleles. Complementary manipulation of panicle shape and seed shattering results in a mechanically stable panicle structure. We propose a stepwise route for the earliest phase of rice domestication, wherein selection of visible SPR3 -controlled closed panicle morphology was instrumental in the sequential recruitment of sh4 and qSH3 , which together led to the loss of shattering.
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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 | 0.000 |
| 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".