Sensitivity of Genotype by Environment Interaction Models to Outlying Observations
Bibliographic record
Abstract
Plant breeding program depends on its ability to provide farmers with genotypes with guaranteed superior performance (phenotype) in terms of yield and/or quality across a range of environmental conditions. To achieve this aim, it is necessary to have an understanding of the model suitable for or leading to a good phenotype. In this study, two cases of scenarios were considered to have a clearer view of the performance of Genotype by Environment Interaction on the following four models; Additive Main Effect and Multiplicative Interaction (AMMI), Finlay Wilkinson (FW), Genotype and Genotype by Environment Interaction (GGE) and Mixed Model. We experiment the inference behind the violation of the assumption of normal distribution by observing the data contamination of two case scenarios (Lowest and Highest outlying observations). It was observed on the two data Types of Balance and Unbalance designs with different Levels of generations. We achieved that by comparative performance of the data contamination techniques under the two case scenarios; Case I scenario was done for Lowest Outlying Observations where 50%, 100% and 500% data contamination on the First Quarter (P1), Mid quarter (P2) and Last Quarter (P3). We then deduced from the result of the model evaluation that, at each levels of data contamination for Balance and Unbalance design, Mixed model was the ideal model for interaction. Case II scenario was done for Highest Outlying Observations where 50%, 100% and 500% data contamination on the First Quarter (P1), Mid quarter (P2) and Last Quarter (P3) were examined on each levels of generations. We then observed from the result of the model evaluation that, at each levels of data contamination for Balance and Unbalance design, Mixed model also outperformed the other three models.
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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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".