Application of graphical analysis and principal components to identify the effect of genotype × trait in maize hybrids
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In order to identify the effect of genotype × trait, 20 maize ( Zea mays L.) hybrids were cultivated and investigated in a randomized complete block design in three replications in the Karaj region. The results of the analysis of variance showed that the effect of genotype in terms of all traits except for the traits of days until the tassel dries, peduncle outside the flag leaf, tassel length, the number of fill seeds, and the depth of the seeds are significantly different. Based on the mean comparison done by Duncan's method, G3, G6, G7, and G4 genotypes were identified as favorable hybrids. Based on the graphic analysis, the genotypes G5, G4, G6, G3, G9, and G14 can be identified as desirable hybrids. The correlation diagram indicated that the grain yield trait has a positive correlation with tassel length, leaf length, leaf width, and leaf surface traits. Based on the principal component analysis, the first 10 components explained more than 74% of the data variance. The traits were classified into 10 components: components of ear characteristics, time characteristics in terms of maturity, leaf characteristics, characteristics of maize plant 1 (cob corn diameter, peduncle length, and grain yield traits), characteristics of maize plant 2 (number of tassel branches, leaf surface, and grain yield traits), physiological characteristics and germination, the crown part of the ear characteristics, grain characteristics, grain yield, and characteristics of the ear head. The experiment results indicated that G8, G15, G1, and G6 hybrids were more favorable in terms of grain yield trait.
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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 | 0.001 | 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) | 0.000 | 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 it