Genetic dissection of a reduced seed-shattering trait acquired in rice domestication
Bibliographic record
Abstract
Asian rice (Oryza sativa L.) was domesticated from wild rice (O. rufipogon Griff.). During rice domestication, the wild characteristic of seed-shattering behaviour was suppressed, enabling an efficient harvest with increased yield. Rice, a stable food for humans, is one of the most important crops consumed by billions of people, especially in Asian countries. With advances in molecular genetic studies, genes or loci involved in reduced seed shattering via the inhibition of abscission layer formation have been identified. The mutations alone showed no inhibitory effect on abscission layer formation in the wild rice O. rufipogon, but their combination enabled a stepwise change in the degree of seed shattering, which may be associated with advances in harvesting tools. In the early stages of rice domestication, the closed panicle formation and slight inhibition of the abscission layer resulted in complementary effects that increased harvesting efficiency. Furthermore, common and distinct loci were found to contribute to reduced seed shattering in groups of rice cultivars, indicating that mutations at seed-shattering loci are important information for tracing the process of rice domestication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".