Agronomic and Ecological Evaluation on Growing Water-Saving and Drought-Resistant Rice (Oryza sativa L.) Through Drip Irrigation
Bibliographic record
Abstract
A field demonstration trial comparing the growth status, yield ability and water use efficiency of drought-tolerant rice (Oryza sativa L.) varieties and normal paddy rice variety under drip irrigation and paddy irrigation was carried out for two years in Shanghai, China. Under drip irrigation, both inbred and hybrid water-saving and drought resistant rice (WDR) varieties showed better yield capacity than paddy rice varieties tested. WDR varieties under drip irrigation attained more than 95% of the yield level that is achieved in paddy field, while the paddy varieties under the same drip condition reached only about 75%.The methane gas emission was obviously decreased under drip irrigation condition, while the emission of other greenhouse gas like nitrous oxide or carbon dioxide was not observed significant difference between drip and paddy irrigation. It could be concluded that it is practicable to grow water saving and drought resistant rice through drip irrigation. Drip irrigation maintained a competitive grain yield and water productivity, and greatly reduced pollution risk to the environment. Considering the conservative amount of fertilizer application, less than the amount of fertilization in normal paddy field, the yield potential of rice could be improved by increasing the amount of fertilizer as top application in drip irrigation system.
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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".