Genetic control of phenotypic plasticity in Asian cultivated and wild rice in response to nutrient and density changes
Bibliographic record
Abstract
Phenotypic plasticity is an adaptive mechanism adopted by plants in response to environmental heterogeneity. Cultivated and wild species adapt in contrasting environments; however, it is not well understood how genetic changes responsible for phenotypic plasticity were involved in crop evolution. We investigated the genetic control of phenotypic plasticity in Asian cultivated (Oryza sativa) and wild rice (O. rufipogon) under 5 environmental conditions (2 nutrient and 3 density levels). Quantitative trait locus (QTL) analysis was conducted for traits affecting plant architecture and biomass production. By analysing the phenotypic means, QTLs of large effects were detected as a cluster on chromosome 7 under all the environmental conditions investigated; this might have contributed to transitions of plant architecture during domestication, as reported previously. Multiple QTLs of plasticity were also found within this QTL cluster, demonstrating that allele-specific environmental sensitivity might control plasticity. Furthermore, QTLs controlling plasticity without affecting phenotypic means were also identified. The mode of action and direction of allele effects of plasticity QTLs varied depending on the traits and environmental signals. These findings confirmed that cultivated and wild rice show distinctive genetic differentiation for phenotypic plasticity, which might have contributed to adaptation under contrasting environmental heterogeneity during the domestication of rice.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".