Application of Graphical Analysis and Principal Components in Investigating the Effect of Genotype × Trait (GT) in Maize Hybrids
Bibliographic record
Abstract
Abstract In order to investigate the effect of genotype x trait and investigate grain yield and yield components and to select the most suitable hybrid in terms of traits, 20 maize hybrids were cultivated and investigated in the form of randomized complete block design (RCBD) in three replications in Karaj region. The results of the analysis of variance at the probability level of 0.01 showed that the effect of genotype in terms of all traits except for the traits of days until 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 and G17, G20, G19 and G18 hybrids were identified as unfavorable hybrids in terms of all evaluated traits. Based on the graphic analysis done on the data, the genotypes G5, G4, G6, G3, G9 and G14 can be identified as desirable hybrids. Also, based on the genotypes grouping diagram, the hybrids were grouped into 9 groups in terms of traits. The correlation diagram between the traits also 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 components analysis, the traits were named into 10 components, which are respectively: components of ear characteristics, time characteristics in terms of maturity, leaf characteristics, Characteristics of maize plant 1, characteristics of maize plant 2, physiological characteristics and germination, the crown part of the ear characteristics, grain characteristics, grain yield and characteristics of the ear head.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".