Cold Tolerance in Rice Plants: Phenotyping Procedures for Physiological Breeding
Bibliographic record
Abstract
The study was conducted using physiological approach to identify rice accessions with superior performance when subjected to infra-optimum temperatures during initial development phase (V2-V4). Forty-two rice genotypes composed by background essentially indica, japonica and indica/japonica cross with a broad genetic and ecological diversity were used. Plants were grown under initial optimum temperatures gradient (OTG - 22/32 °C night/day) until V2 stage; subsequently were subjected to infra-optimum temperature gradient (ITG - 13/17 °C night/day) during three days; after all genotypes returned to OTG conditions for seven days to recovery. Principal components analysis (PCA) highlighted that the three principal components account for 75.16% of total variation at the end of evaluated period. There were similar contributions of effective quantum yield (Y(II) - stress) and electron transport rate variables after recovery period (ETR - recovery) for PC1. Interestedly, genotypes highly responsive under initial OTG which showed fast initial biomass accumulation were also highly sensitive to stress when subjected to ITG, with accentuated decreases in their physiological performance. Sel. TB 1211-3 line, CTB 1419, CTB 1444, CTB 1455 and AB 13720 progenies showed greater performance for physiological analyzed variables, being potentially useful for breeding efforts aiming improve cold tolerance in rice at initial phase.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".