Impact of Supra-Optimal Temperatures on Physiology and Yield in Rice Field
Bibliographic record
Abstract
Heat stress is an increasing constraint for the productivity of rice (Oryza sativa L.) worldwide. In this context, a study was carry out to quantify the supra-optimal temperature effects on rice yield-components and secondly to investigate its effects on plant physiological attributes when different genetic backgrounds are submitted to contrasting environment air-temperatures during the anthesis, the most sensitive growth phase to heat stress. Three Embrapa's cultivars were used, BRS Pampa, BRS Sinuelo CL and IAS 12-9 Formosa which represent indica/japonica, indica and essentially japonica subspecies, respectively. One day before anthesis phase, sub plot plants were submitted to heat stress via polythene shelters for 96 h. Photosynthesis and respiration parameters were measured at 24 and 48 h after stress, and at physiological maturity, grain carbon isotope fractionation as well as yield components and grain yield were quantified. There were significant differences among genotypes for some gas exchange parameters at ambient and under increased temperatures at 24 and 48 h after stress, such as carbon assimilation and respiration rate. Heat-stress also affected yield components, especially for BRS Sinuelo CL showing the highest spikelet sterility (54%) while BRS Pampa had the lowest value (20.80%) and the highest 1000-grain weight and grain yield. These results demonstrate that although heat tolerance has been more frequently found in indica spp, this trait can also be present in genotypes combining indica/japonica genetic background, as shown by BRS Pampa cultivar.
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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) | 0.001 | 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".