Effects of Cultivar, Growing Location, and Year on Physicochemical and Cooking Characteristics of Dry Beans (<i>Phaseolus vulgaris</i>)
Bibliographic record
Abstract
The effects of cultivar, growing location, and year on physicochemical and cooking characteristics of beans ( Phaseolus vulgaris ) were investigated, and the relationship between these characteristics was determined. Twenty dry bean cultivars and breeding lines were grown at two different locations for two consecutive years (2013 and 2014) in southern Manitoba, Canada. Results indicated that cultivar, growing location, and year had significant effects on seed weight, water hydration capacity, and cooking time of beans. Significant cultivar, location, and year variations in protein, starch, and phytic acid contents in beans were observed. Most of the traits were also significantly affected by the interactions of cultivar × location, cultivar × year, and location × year. Seed weight was negatively correlated with crude protein and ash contents but positively correlated with starch content. Cooking time was negatively correlated with protein, ash, and phytic acid contents but positively correlated with firmness. Phytic acid content in beans was positively correlated with ash content. Knowledge gained from this study will be useful to bean breeders in selecting parental lines for crossing and cultivar development in efforts to improve the quality of beans.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".