Study on Optimization for High Yield Population of Rice Mainly Planted in Sanjiang Region
Bibliographic record
Abstract
Adopting split-plot design,optimization for high yield rice population was carried out on 4 cultivars mainly planted in Sanjiang region in 2009-2010.Row spacing,plant spacing and seedling number per hill of these cultivars for high yielding cultivation were defined.The results indicated that 5-8 seedlings per hill,10 cm plant spacing and 24 cm row spacing could obtain high yield in this region.Compared with conventional production(4 seedlings per hill,13.3 cm plant spacing and 30 cm row spacing),the theoretical yield of Longjing 20 and Kenjing 3 cultivated in agricultural scientific research institute of Jiansanjiang was increased by 43.1% and 29.4% respectively,and the difference in them was highly significant.The theoretical yield of Kongyu 131 and Longjing 26 cultivated in the research and development centre of seven stars was higher than that of conventional production(increased 57.3%,47.9%),and the difference in them was also highly significant.Plant spacing and row spacing were negatively correlated with yield,and most of negative correlation between plant spacing and yield reached significant or highly significant level.Plant spacing was the most important limiting factor for high yield in the three factors.Therefore,agricultural machinery should be adjusted,which is full use of local natural resources to achieve the most efficient rice yield,a very cost-effective technical measures in Sanjiang region.
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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 | 0.000 | 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 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".