Identification and Characterization of Chromosome Regions Associated With Salinity Tolerance in Rice
Bibliographic record
Abstract
Rice, the major crop sustaining approximately half the world population, has been extensively reported to be sensitive to saline conditions. However, the genetic and physiological understanding related to long-term salinity stress remains unclear so far. The aim of this study was to evaluate the mechanisms of salinity tolerance in a salinity-tolerant variety of rice, Nona Bokra, and to detect the chromosomal regions responsible for it. We utilized chromosome segment substitution lines (CSSLs) carrying segments from Nona Bokra in the genetic background of a salt-sensitive variety Koshihikari by investigating the plant growth, grain productivity, and ion contents in plants subjected to long-term salinity stress. Comparison of plant growth and grain yield of CSSLs grown under long-term saline conditions suggests that the salinity tolerance of Nona Bokra involves the improvement of plant dry matter, panicle number, and percentage of ripened grains. Nona Bokra has the chromosomal regions for the improvement of the panicle number on chromosome 2 and the percentage of ripened grains on chromosome 6 or 10 under salinity conditions. It was suggested that these chromosomal regions were related to Na+ and Cl- exclusion. Low Na+ and Cl- contents in whole plant at full heading stage would be vital for improving the yield under long-term saline 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".