Environment Stratification in the Evaluation of Corn Hybrids in Southern Brazil
Bibliographic record
Abstract
The implementation of a network of maize trials is an onerous task, so breeding programs seek to eliminate redundant environments, remaining only contrasting ones. The objective was to perform the homogeneous environment grouping by studying the G×E interaction through different environmental stratification methods, as well as to compare the efficiency of these methods. Four methods were used: environmental dissimilarity (Djj); Decomposition of the genotype × environment interaction (G×E) into simple and complex parts by Cruz and Castoldi; Pearson correlation coefficient; and factor analysis. Twenty-five single-cross corn hybrids and three commercial cultivars were tested in eight cultivation environments in a randomized complete block design with three replicates in the evaluation of grain yield. Quedas do Iguaçu and Cascavel can be reduced into only one test environment by methods of factor analysis and Pearson correlation. The environments of Pato Branco and Ampére are grouped by two of the four methods employed. However, Clevelândia, Londrina, and Coxilha do not group with other environments in three of the four methods, and Itapiranga in two of the four methods. Cruz and Castoldi’s method and stratification by factor analysis are more rigorous in grouping criteria.
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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 | 0.003 | 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 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".