Genotype × Environment Interaction and Stability for Isoflavone Content in Soybean
Bibliographic record
Abstract
Isoflavones are naturally occurring compounds found in soybean [Glycine max (L.) Merr.]. Soybean isoflavone, as a quantitative trait, is subject to significant genotype × environment interaction, which makes breeding for this trait difficult. Thirty F4:7 soybean lines, derived from crosses of ‘RCAT Angora’ × CK‐01 and ‘Heinong 35’ × RCAT Angora were classified within each population as high, intermediate, or low isoflavone. The lines, parents, and two maturity checks were grown in four locations in 2005 and six locations in 2006 across Ontario and Quebec, Canada. Isoflavone content of the mature seed was determined by near‐infrared reflectance. The effects of genotype, environment, and the genotype × environment (G × E) interaction were significant. Consistently performing genotypes from the two populations were identified by several stability parameters. Genotype–genotype × environment (GGE) biplot demonstrated an ability to provide information on both the genotypes and the environments in which they were evaluated. The identification of genotypes with consistent placement in either the high‐ and low‐isoflavone classes suggested that breeding for relative isoflavone content in soybean is possible, although breeding for absolute stability remains a challenge, given the large environmental influence on soybean isoflavone levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".