Using Factor Analytic Models for Joining Environments and Genotypes without Crossover Genotype × Environment Interaction
Bibliographic record
Abstract
Genotype × environment interaction variability can be due to crossover interaction (COI) or to non‐COI. Statistical methods for detecting and quantifying COI and for forming subsets of environments and/or genotypes with negligible COI have been based on fixed effects linear–bilinear models. Linear mixed models and the factor analytic (FA) variance–covariance structure offer a more realistic and effective approach for quantifying COI and forming subsets of environments and genotypes without COI. The main objectives of this study are (i) to present an integrated methodology for clustering environments and genotypes with negligible COI based on results obtained from fitting FA to multi‐environment trial (MET) data; and (ii) to detect COI using predictable functions based on the linear mixed model with FA and Best Linear Unbiased Prediction (BLUP) of genotypes. Two CIMMYT maize ( Zea mays L.) international METs are used to illustrate the method for searching for subsets of environments and genotypes with negligible COI. Results from both data sets showed that the proposed method formed subsets of environments and/or genotypes with negligible COI. The main advantage of the integrated approach is that one unique linear mixed model, the FA model, can be used for (i) modeling the association among environments; (ii) forming subsets of environments without COI; (iii) grouping genotypes into non‐COI subsets; and (iv) detecting COI using the appropriate predictable function.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".