Bibliographic record
Abstract
Variation of genotype–environment interactions can be divided to determine whether or not the interactions involve change in genotype or cultivar ranks across environments. However, no sound statistical tests are available for such determination. In this study, the restricted maximum likelihood (REML) analysis based on the mixed models theory was used to estimate genetic parameters and to test statistically for causes of genotype–environment interactions in two wheat (Triticum aestivum L.) crosses, Potam × Ingal and RL4137 × Ingal. The data with each cross consisted of the measurements of five quantitative traits for 144 F3‐derived F5 and F6 lines from 48 F2 families evaluated at Saskatoon in 1986 and 1987, respectively. The causes of family × year or line × year interactions were tested by comparing log likelihoods of reduced and full models (i.e., the family or line covariance structures with and without constraints). The REML estimation guaranteed that an estimated family or line covariance matrix was positive definite. Significant line × year interactions were detected in three traits in RL4137 × Ingal only and none involved rank changes. Significant family × year interactions were found in seven of 10 cross‐trait cases, but four of those seven cases involved change in family ranks across the 2 yr. The REML analysis allows the development of sound statistical tests for the different causes of interactions and constraining estimated genetic variances and covariances within acceptable ranges, thereby effectively removing the deficiencies with the conventional multivariate analysis of variance method.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".