Studies on the Stability of Kernel Weight and Oil Percentage of the Micro-endosperm Maize with Opaque-2 Gene
Bibliographic record
Abstract
6 micro-endosperm maize materials with opaque-2( o2) gene( o2 micro-endosperm maize for short) were used in this study,which had been inbred 4 generations to obtain S1,S2,S3and S4. A part of grains were selected from S1,S3and S4in random sampling,kernel weight and oil percentage were measured,the difference of these strains were compared on these traits among the different generations,the relation of these traits among each generation were analyzed. The changes of these strains in o2 micro-endosperm maize with the increase of the inbred generation were studied. The main results were as follows:( 1) The results of the kernel weight showed that: with the increase of inbred generation,the kernel weight of the various strains in o2 micro-endosperm maize showed different change,but the correlation coefficient wasn't significant at 0. 05 level. The total mean of kernel weight was decreased with the increase of inbred generation.( 2) The oil percentage of the o2 micro-endosperm maize for strains 1,4,5,6 were increased with the increase of inbred generation. The oil percentage of strain 2 and strain 3 were reduced in the S3and increased in the S4,only the oil percentage of strain 3 in the S4was lower than that in the S1.( 3) The correlation analysis between every inbred generation each other about these traits of o2 micro-endosperm maize were made,then the results indicated that,the correlation coefficient of kernel weight between every inbred generation were positive,but the r wasn't significant at 0. 05 level. The correlation coefficient of oil percentage between the S1and the S3,the S3and the S4were positive, the r wasn't significant at 0. 05 level. The correlation coefficient of oil percentage between the S1and the S4 was significant at 0. 05 level.
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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".