Study on Breeding Strategy of High Yield of Hybrid Late Season Rice in the South Rice Area of Middle Reaches of Yangtze
Bibliographic record
Abstract
The trial population is composed of 61 hybrid late season rice combinations.The relationship of the grain yield per unit of area and yield correlated characters was analyzed by synthetically utilizing many kinds of statistical methods.It is attempted to provide scientific basis for high yield breeding of hybrid late season rice in the south rice area of middle reaches of Yangtze,particularly in Jiangxi ecological condition.The results indicated that,in the situation that the grain yield had achieved a higher level,the direction of high yield breeding in the hybrid late season rice was to emphatically select and match combination with more spikelets per panicle and big grain.In addition,it must seek unisonous combination of effective panicle number and more spikelets per panicle and big grain on the basis of higher effective panicle number.The new combination of hybrid late season rice was selected through high productive tiller percentage,which had positive effect to improve the yield level of new combination.Moreover,productive tiller percentage can be used as a core character to coordinate the contradiction of many effective panicles with more spikelets per panicle and big grain,then the combination of effective panicles and more spikelets per panicle and big grain in higher level is realized,thus a higher grain yield is obtained.On the base of realizing the unisonous combination of more effective panicles and more spikelets per panicle and big grain in a higher level,the improvement of filled grains percentage is also essential taken into consideration.The key point is to break the disadvantageous correlation of filled grains percentage and spikelets per panicle.
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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".