Genotypic Association of Parameters Commonly Used to Predict Canning Quality of Dry Bean
Bibliographic record
Abstract
ABSTRACT To select dry bean ( Phaseolus vulgaris L.) varieties suitable for the canning and processing industry, it is essential to understand the genetic and environmental effects on the quality parameters as well as their associations. A dataset of 6 yr of dry bean quality evaluations in the multi‐location provincial registration trials in Ontario, Canada, with data for parameters routinely used by breeding programs to predict the final canning quality of breeding materials was used. Genetic, environmental, and genotype (G) × environmental effects and multi‐variable associations were studied using the estimates of the genotypic least squared means for the balanced yearly data and the best linear unbiased predictors (BLUPs) for the overall unbalanced data. Genetic effects, accounting for 35 to 76% of the variation in navy bean and 38 to 88% of the variation in large‐seeded bean, followed by location (L) effects, accounting for 11 to 66% of the variation in navy bean and 11 to 64% of the variation in large‐seeded bean, were more important than the genotype × location (GL) interaction effects for seed composition parameters. This was not the case for physical and texture parameters, where GL interaction effects were often most important. Positive associations were observed among hydration coefficient, can yield, and protein content in the yearly analyses as well as in the multi‐year analysis. The negative association between percent washed‐drain solids and bean texture after processing was also repeatedly observed over years. The associations reported here may guide further selection efforts in bean breeding programs.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 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".