Edible Grain Legumes
Bibliographic record
Abstract
Edible grain legumes, including dry bean (Phaseolus vulgaris L.), dry pea (Pisum sativum L.), chickpea (Cicer arientinum L.), and lentil (Lens culinaris Medikus), have served as important sources of protein in the human diet for thousands of years. In the United States, these crops are consumed nationally and produced for export markets. The objectives of this study were to examine yield gains in edible grain legume crops over the past 25 yr. Genetic gain in dry bean during the past 30 yr based on common trials was 13.9 kg ha−1 yr−1 (0.77% yr−1) and 17.4 kg ha−1 yr−1 (0.85% yr−1) for navy and pinto bean cultivars, respectively. Data from national yield trials on research sites indicates that yield gains were 0.4, 0.7, 0.9, and 1.7% for pinto, navy, black, and kidney beans, respectively. The results also suggest that dry bean cultivars have not reached a yield plateau for most market classes. Continued introgression of germplasm from other races of common bean should provide new sources of genetic diversity to enhance yield in the future. Over the past 25 yr, the production of cool season food legumes (pea, lentil, and chickpea) in the United States has increased dramatically; however, yields of dry pea in the United States have decreased by 0.3% per year, lentil yields have increased by only 0.1% per year, and chickpea yields have increased by 2.8% per year. Pea and lentil production has increased dramatically in Montana and North Dakota, but the cultivars grown in this region were originally developed in the U.S. Pacific Northwest (PNW) and Canada and are likely not well adapted for Montana and North Dakota. Several currently grown cultivars are at least 20 yr old, but new cultivars have been released that are superior to these older cultivars.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".