Selection of maize hybrids based on genotype × yield × trait (GYT) in different environments
Bibliographic record
Abstract
This study aimed to identify the best genotypes using the genotype × yield × trait (GYT) method. To investigate the relationships was performed between yield × traits in four regions of Karaj, Birjand, Shiraz and Arak in two cropping years in a randomized complete block design (RCBD) with three replications. The average grain yield in four regions and two years of the experiment was calculated as 5966 kg/ha, and GYT was obtained based on the multiplication of grain yield with different traits. Comparing the average effect of genotype × year in different environments showed that KSC703 and KSC707 hybrids are among the most productive hybrids among the studied genotypes in grain yield. By examining the correlation coefficients between yield × traits in the tested areas, Y × TWG with Y × GW, Y × NRE, Y × NGR and Y × EL, Y × ED with Y × NGR, Y × NRE with Y × GW and the combination of Y × GW with Y × GL had a positive and significant correlation in all regions. The correlation diagrams were drawn on the evaluated areas' data and showed the correlation of most compounds except Y × GT with each other. Based on the analysis of the main components, the first three components explained the greatest diversity in the population. They were named the component ear grain profile, grain thickness component and plant height profile component.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".