Bibliographic record
Abstract
Abstract The responses of different genotypes to an environmental gradient are often nonlinear and nonparallel. Current tests for differential genotypic responses are based largely on linear regression models (stability analysis) or on evaluations of all quadruples for crossover interactions (COIs) from a two‐way genotype × environment (G × E) table if the environments are unquantified. The objective of this study was to develop a new statistical analysis for comparing nonlinear genotypic response curves over an environmental gradient. We first conducted an investigation to find the points where the two nonparallel curves intersected. If the intersection points lie within the attainable environmental range, the two nonparallel curves involve COI; if the points lie at the boundaries or outside the attainable range, the nonparallel curves do not involve COI. We then developed statistical tests for comparing a full and a reduced model describing the two nonparallel curves. The tests were used to analyze a wheat ( Triticum aestivum L.) germination test (WGT) data under the reciprocal of a linear function and a barley ( Hordeum vulgare L.) cultivar trial (BCT) data under Cauchy function. The WGT analysis shows that at least one pair of cultivars involve COI over the temperature gradient, which went undetected by a previous test based on all possible quadruples. The BCT analysis revealed that 56% of 780 possible pairs of 40 genotypes differ significantly from each other, providing more insights into the patterns of complex G × E interactions. Our analysis is therefore a viable alternative to the existing procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".