Influence of Water Management and Silica Application on Rice Growth and Productivity in Central Java, Indonesia
Bibliographic record
Abstract
Rice cultivation in our study site at Central Java, Indonesia, is constrained by water scarcity and blast disease problems. A field experiment was thus conducted to evaluate the effect of water management and silicon (Si) application (with 500 kg ha-1 of silica gel) on improving rice growth and productivity and blast disease infection in Jakenan, Central Java. Split plot in randomized complete block design with 4 replications was used. The results showed that two water saving methods, intermittent (IT) and aerobic rice (AR) increased rice yield compared to conventional flooding water management. Further, IT showed better root growth and hence lodging resistance and decreased blast disease infection. IT had higher yield potential compared to AR although the yield of IT and AR were not statistically different. Si application gave significant effect on reducing leaf and neck blast infection and also increased stomata density (p < 0.01) in all water treatments. Si application did not result in increased yield but exhibited potential towards improving rice plant growth and production. Since Si fertilizer was never used in rice cultivation in Indonesia, the study reveals that IT combine with Si application was a suitable management for rice production in dry season in water limited Central Java region.
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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".