Yield stability indices of primocane-fruiting red raspberry (<i>Rubus idaeus</i> L.)
Bibliographic record
Abstract
Three primocane-fruiting cultivars were grown over 14 location/years (environments) to assess their relative yield stability using three different statistical models. The models varied in their definition of stability and sometimes produced conflicting results. In general, ranking of the three cultivars from most to least stable was Heritage, Redwing and Autumn Bliss. Although Redwing and Autumn Bliss had better productivity than Heritage, both cultivars had a large portion of genotype × environment variance attributed to unpredictable elements according to some stability analyses. This variation was characterized by large and significant deviations from linearity. No single model adequately characterized stability in these cultivars. The three cultivars varied in productivity across the 14 environments, with Heritage always yielding less than the two other cultivars. Key words: Breeding, fall-bearing, genotype × environment, yield stability
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".