Comparative Analysis of Perennial and Annual Phaseolus Seed Nutrient Concentrations
Bibliographic record
Abstract
Long-term agricultural sustainability is dependent in part on our capacity to provide productive, nutritious crops that minimize the negative impacts of agriculture on the landscape. Perennial grains within an agroforestry context offers one solution: These plants produce large root systems that reduce soil erosion and simultaneously have the potential to produce nutrients to combat malnutrition. However, nutrient compositions of wild, perennial, herbaceous species, such as those related to the common bean (Phaseolus vulgaris) are not well known. In this study, seed ion and amino acid concentrations of perennial and annual Phaseolus species were quantified using ionomics and mass spectrometry. No statistical difference was observed for Zn, toxic ions (e.g., As) or essential amino acid concentrations (except threonine) between perennial and annual Phaseolus species. However, differences were observed for some nutritionally important ions. For example, Ca, Cu, Fe, Mg, Mn, and P concentrations were higher in annual species; further, ion and amino acid concentrations appear to be largely independent of each other. These results suggest variability in ion and amino acid concentrations exist in Phaseolus. As new crop candidates are considered for ecological services, nutritional quality should be optimized to maximize nutrient output of sustainable food crops.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".