Genotype/Genotype × Environment Biplot Analysis for Cultivar Evaluation and Mega-environment Investigation in Primocane-fruiting Red Raspberry
Bibliographic record
Abstract
Primocane-fruiting (PF) red raspberry (Rubus idaeus L.) cultivars are being grown in many regions as their popularity increases. However, testing of this perennial fruit crop is expensive and requires many years. Large genotype (G) × environment (E) interactions can make identification of superior genotypes difficult. The G/G × E (GGE) biplot can be used to measure cultivar performance and group locations into mega-environments. The GGE biplot was applied to yield trial data of three PF red raspberry cultivars Autumn Bliss, Heritage, and Redwing grown in 17 environments (year-location combinations). The 17 environments encompassed six locations in Ontario and Quebec, Canada between 1989 and 1996. `Autumn Bliss' produced the highest yields in 11 of 17 environments. `Heritage' was usually the lowest yielding cultivar. Two mega-environments were identified based on the performance of `Autumn Bliss' and `Redwing'. Some environmental variables were likely to be responsible for the discriminating ability of the test environments as they were correlated with the primary effects. The GGE biplot was an effective analysis to determine mega-environments and the cultivars best adapted to each.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".