Breeding upland rice for drought resistance
Bibliographic record
Abstract
Abstract Upland rice, produced by smallholder farmers, is the lowest‐yielding rice production system. Drought stress is the most severe abiotic constraint in upland rice. Improving productivity of rice in the upland ecosystem is essential to meet rice food security needs of impoverished upland communities. Breeding drought‐resistant upland rice is therefore an increasingly important goal. Numerous secondary characters have been suggested to help plant breeders in their selections. Most of these traits are not used in selection, as they are not practical for selection purposes, exhibit low heritability, or are not highly correlated with grain yield. The use of managed drought stress, where drought stress can be imposed at specific periods, has been shown to increase the heritability of yield under stress to values similar to those obtained for yield in well‐watered conditions. It has now been demonstrated that drought‐tolerant upland rice can be bred by directly selecting for yield in stress environments. The use of molecular markers to perform selection may eventually provide plant breeders with more efficient selection methods. To date, many quantitative trait loci (QTL) for drought resistance have been identified in rice, but few are suitable for use in marker‐assisted selection. However, large‐effect drought resistance QTL have now been identified and may enable effective use of marker‐assisted selection for drought resistance. Copyright © 2008 Society of Chemical Industry
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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) | 0.002 | 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".