Rice (Oryza sativa L.) yield and potassium use efficiency as affected by potassium fertilizer sources
Bibliographic record
Abstract
Enhancing potassium use efficiency (KUE) is vital for sustainable rice production. In a pot experiment, T1 (Canada origin) significantly outperformed T2 (Morocco origin) in grain yield for BRRI dhan 61 (7205 kg ha-1), BRRI dhan 68 (6050 kg ha-1), and BRRI dhan 28 (4227.3 kg ha-1). BRRI 61 exhibited superior filled grain and 1000 grain weight under T1. In T1-treated soil, BRRI 61 recorded the highest grain K uptake (63.56 kg ha-1), surpassing BRRI 68 and BRRI 28. With T1, BRRI 61 demonstrated 1.24 and 2.70 times greater agronomic efficiency than BRRI 68 and BRRI 28, respectively, along with a crop recovery efficiency of 0.42. T1 treatment improved agronomic traits across varieties. Recommending T1 fertilizer for BRRI 61 is suggested to optimize yield, K uptake, and use efficiency, emphasizing its potential for sustainable rice cultivation.
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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".