Rice Yield Components under Water Stress Imposed at Different Growth Stages
Bibliographic record
Abstract
We aimed to assess rice yield components as function of water stress imposed at distinct crop growth stages under greenhouse, in randomized block design under factorial scheme 3 × 4 + 1, with four replications. The factor “A” was the growth stage in which water stress was imposed, being (a) vegetative, (b) reproductive 1, and (c) reproductive 2; factor “B” was four levels of water stress (0-200 kPa). There was also an additional treatment consisting of a flooded check. Water was replenished to saturation every time the threshold stress was reached. At the end of the cycle, all panicles were counted and collected, per plant, for further analysis. In the lab, grains per panicle were counted, being classified either as whole kernel or aborted grains, whose results were used for obtaining sterility percentage. Whole kernel grains were weighted for obtaining the 1000 grains weight for each treatment, and the consequent plant grain yields. Number of panicles was not affected when stress was imposed after panicle initiation, but when imposed at tillering it was reduced; at grain filling, water stress promoted grains sterility higher than 90%; grain weight was only reduced when carbohydrates were directed to root formation in detriment of grain filling; lower rice grain yield per plant was observed even when treatments were maintained above 10kPa all along the cycle, compared to the flooded treatment.
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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) | 0.001 | 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".