Identification and physiological characterization of two sister lines of <i>indica</i> rice (<i>Oryza sativa</i> L.) with contrasting levels of cold tolerance
Bibliographic record
Abstract
Exposure to low temperature during germination and vegetative growth is a limiting factor to the establishment and development of rice seedlings. Higher cold tolerance of japonica than indica subspecies is well documented. However, reports of cold tolerance in indica genotypes are rare. We screened a large number of indica rice genotypes for cold tolerance during germination and initial vegetative growth. The indica genotypes IRGA 959-1-2-2F-4-1-4-A and IRGA 959-1-2-2F-4-1-4-D-1-CA-1, derived from the same cross, were characterized, respectively, as tolerant and sensitive to low temperature. Indexes of photosynthetic performance during light absorption were heavily affected by cold in both genotypes, but recovered after cold exposure only in the tolerant genotype. Activities of the antioxidant enzymes SOD and CAT (at the vegetative stage) and CAT and APX (at the germination stage) were higher in the tolerant than in the sensitive genotype. Expression of 20 genes previously related to cold response in rice was evaluated. Expression of OsLIP9 and OsWCOR413 were higher in the tolerant genotype upon or prior to cold exposure, respectively. The two sister lines show different molecular and physiological responses to low temperature stress. Further in-depth studies with these lines may help to identify new cold tolerance mechanisms in 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".