Bibliographic record
Abstract
Genotype–environment interactions (GEI) are important in crop improvement if genotype ranks change across environments. Current tests for crossover (rank changing) interactions (COI) assume that effects are all fixed or all random. The objective of this study was to develop a new test for COI under the model with a mixture of fixed and random genotypic, environmental, and GEI effects. The key part of this new test is that the difference between a pair of genotypes at a random environment or the difference between a pair of environments for a random genotype involves the linear combinations (predictable functions) of both best linear unbiased estimates (BLUEs) of fixed effects and best linear unbiased predictors (BLUPs) of random effects. The predictable functions are used in the same way as the usual estimable functions for the fixed effects in hypothesis testing except that the BLUPs of random effects are adjusted by accounting for the uncertainty arising from the distributions of these effects. Strategies are proposed to implement the procedure using the SAS system. The procedure was used to analyze barley (Hordeum vulgare L.) and field pea (Pisum sativum L.) cultivar trials. The analyses show that treating random effects as fixed, as may happen with previous analysis procedures, results in detection of more COI than mixedߚ or random‐effect models. Therefore, significant COI may be overemphasized when random GEI effects are treated as fixed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".