High night temperature and plant growth regulator effects on spikelet sterility, grain characteristics and yield of rice (<i>Oryza sativa</i> L.) plants
Bibliographic record
Abstract
Mohammed, A. R. and Tarpley, L. 2011. High night temperature and plant growth regulator effects on spikelet sterility, grain characteristics and yield of rice ( Oryza sativa L.) plants. Can. J. Plant Sci. 91: 283–291. The presence of seasonally high night temperature (HNT) as a result of global warming, occurring during the critical stages of development, could reduce rice yield and quality. To understand how a combination of HNT and plant growth regulators (PGR; α-tocopherol and glycine betaine) affects yield and yield-related parameters of rice plants, we conducted three pot experiments under two levels of night temperature (NT; 27 and 32oC) with or without PGR treatments. Plants were subjected to a HNT through the use of continuously controlled infrared heaters, starting 20 d after emergence (DAE), from 2000 until 0600. Plants were treated with α-tocopherol and glycine betaine 20 DAE. The NT had no effect on number of productive tillers, main-stem panicle length or number of primary branches per panicle; however, reduced yield resulted from significant effects on spikelet sterility (SS), and grain length, width, and weight. The grains located at the base of the panicle showed decreased length and width compared with grains located at the tip of the panicle. Application of glycine betaine increased grain yield by predominantly decreasing SS in rice plants, suggesting the potential future use of glycine betaine to help partially prevent HNT damage to 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".