Study on Limiting Factors of Influencing Hybrid Rice Ear and High Yield Cultival Ways
Bibliographic record
Abstract
With II you No.7 as material to study the limiting factors of influencing hybrid rice earbearing and high yield cultival ways by hole application stage,hole application standard and triagle application N fertilizer.The results showed that the period before or behind seedlings number up to the most was the most sensitive period for influencing rice percentage of earbearing tiller.In all kinds of factors,light was the lead factor in this phase.The effect of increasing earbearing tiller percentage by adding nitrogen less than by improving light,increasing nitrogen could increase significantly percentage of earbearing tiller on condition that light condition of colony basal improved.Adopted triangle cultivation seedlings could improve colony basal light condition.In all,triangle cultivation seedlings and applying nitrogen at n-2 period was relatively good measures for rice production.Through these measures,seedlings became strong and number decreased significantly,percentage of earbearing tiller and number of grains per ear increased,led to increasing yield finally,but the extent of yield increasing correlated with soil fertility.
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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) | 0.001 | 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".