Current Situation of Wheat Yield and Quality Improvement in Huang-huai Winter Wheat Region
Bibliographic record
Abstract
To understand the present situation and problems of wheat breeding in Huang-huai winter wheat region(HHWWR),the yield components and quality parameters of 68 wheat varieties,which passed through the HHWWR yield trial in last ten years,were analyzed.The results indicated that yield and its components of wheat varieties ascended gradually.It was found that the grains per ear played the most important role for yield increase since the standardized coefficients of grains per ear,effective ears/ha and one thousand grain weight was 1.149,1.06 and 0.793,respectively.At the same time,the wheat quality was not improved but deteriorated to some extent.It showed that,through the analysis of the registered and extended varieties,the quality parameters of most varieties reached or exceeded the parameters of the U.S.and Canadian wheat varieties in some years or some sites;however,the quality parameters were not stable in different years and sites.In addition,it also found that the frequency of subunits 5+10 related with good quality was very low while that of the 1BL/1RS translocation was considerably high among the current varieties,lines and parent materials analyzed.As the breeding strategies,much attention should pay to the increase of grains per ear in high yield breeding and subunits 5+10 in quality breeding;moreover,the yield and quality should be improved simultaneously.
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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 | 0.001 |
| 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".