Application of Nitrogen and Silicon Rates on Morphological and Chemical Lodging Related Characteristics in Rice (Oryza sativa L.) at North of Iran
Bibliographic record
Abstract
Rice-based irrigated lowlands are the major cropping system in north of Iran. This experiment was carried out in split plot in basis of randomized complete block design with three replications at north of Iran in 2010. Main plot was nitrogen rates including (0, 50, 100 and 150 kg N ha-1) applied as urea and sub plot was silicon rates (0, 300 and 600 kg ha-1) applied as calcium silicate. Results showed that minimum of the plant height, flag leaf length, fourth inter-node bending moment and grain yield (4350 kg ha-1) were obtained at N0, as well as the maximum of the plant height, panicle length, flag leaf length, third inter-node length were observed at N100 and N150, respectively. But the highest of bending moment obtained for fourth inter-node and maximum grain yield (6063 kg ha-1) was observed in N150. Treatment Si600 had increased significantly over control in plant height, stem length, panicle length, third inter-node length, third inter-node bending moment, cellulose, hemi-cellulose and lignin in relation to 7.76, 9.91, 30.18, 31.03, 18.71, 7.60, 34.50 and 26.26 %, respectively. Therefore treatment with N150 and Si600 had shown best results for agronomical indices and grain yield.
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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".