Evaluating Drought Tolerance in African Rice Genotypes Across Upland and Lowland Ecologies in Nigeria
Bibliographic record
Abstract
Rice is a staple for over half the world’s population. However, its productivity is severely limited by drought stress. In this study, we screened 16 upland and 25 lowland genotypes under well-watered (WW) and drought stress (DS) conditions. Grain yield was strongly and positively correlated with vegetative vigor, panicle dry weight, harvest index, chlorophyll content, and Quantum yield of Photosystem II under DS in both the environments. Many upland genotypes maintained high yields under drought ~1637-2242 kg/ha compared to ~2016-3629 kg/ha under WW. The most promising lowland genotypes yielded ~2600-2900 kg/ha under DS compared to 4600-6300 under WW. Among the five genotypes that were tested in both upland and lowland conditions, two performed well in both environments, suggesting adaptability to both ecologies. Some genotypes showed high post-drought recovery (up to ~90%), indicating that they are suitable for border testing in drought prone environments to check stability and potential lines useful for breeding drought tolerance in rice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".