The coordinated increase in stomatal density and vein dimensions during genetic improvement in rice
Bibliographic record
Abstract
Abstract Rice (Oryza sativa L. ssp. indica) has experienced three distinct phases of considerable yield increases: Green Revolution, utilization of heterosis, and the combination of ideotype and inter‐subspecific hybrid breeding. Crop breeding and selection for high yield have increased radiation use efficiency in modern indica rice varieties. However, the underlying leaf morphological and physiological changes have not been established. Field and pot experiments were conducted in 2016 and 2017. We investigated the relationships between the anatomical maximum stomatal conductance (gmax), operational stomatal conductance (gop), and the anatomy of the stomata and vein in relation to leaf‐level transpiration and photosynthesis across historical indica rice varieties. The results showed that flag leaf temperature of new varieties was reduced relative to the temperature of older varieties due to increased gop and leaf transpirational cooling. Both high stomatal density and larger veins were increased in new varieties with improved yield potential, while no change was observed in stomatal length and vein density. There was a significant correlation between stomatal density and gop as well as between gop and the light‐saturated photosynthetic rate. The present study reveals that historical selection for high yield is accompanied by leaf morphological changes that contribute to enhanced gop, leaf cooling, and photosynthesis of irrigated rice inhabiting hot, high light environments.
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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) | 0.000 | 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".