Adaptability and Stability in Maize Populations
Bibliographic record
Abstract
Maize plays an important role in the national and global economy, continuously increasing its total production due to advances in technology and access to new land areas. Thus, new sources of germplasm are fundamental to generate cultivars more adapted to the diversity of environments and planting times. The objective of this study was to evaluate 36 populations of maize in three environments, aiming to identify the existence of genotype-by-environment interaction, classify populations based on adaptability and stability using the methods of regression and mixed models, indicate the best populations, and compare the two methodologies. The environments evaluated were: E1-second crop (safrinha) season of 2016 in an experimental area of latosol, with incidence of water stress; E2-crop season 2016/2017 in sandy soil, in family farm area; and E3-crop season 2016/2017 in an experimental area of latosol, no incidence of water stress. Grain yield was evaluated, adaptability and stability analysis was performed. Population 36 achieved high productivity, adaptability and general stability in three tested environments. Both methodologies showed similar results regarding adaptability and stability of some populations in three environments, but mixed models were more suitable for providing better selective accuracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".