Seed Mineral Composition and Protein Content of Faba Beans (Vicia faba L.) with Contrasting Tannin Contents
Bibliographic record
Abstract
Two-thirds of the world’s population are at risk of deficiency in one or more essential mineral elements. The high concentrations of essential mineral elements in pulse seeds are fundamentally important to human and animal nutrition. In this study, seeds of 25 genotypes of faba bean (12 low-tannin and 13 normal-tannin genotypes) were evaluated for mineral nutrients and protein content in three locations in Western Canada during 2016–2017. Seed mineral concentrations were examined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and the protein content was determined by Near-Infrared (NIR) spectroscopy. Location and year (site-year) effects were significant for all studied minerals, with less effect for calcium (Ca) and protein content. Genotype by environment interactions were found to be small for magnesium (Mg), cobalt (Co), Ca, zinc (Zn), and protein content. Higher seed concentrations of Ca, manganese (Mn), Mg, and cadmium (Cd) were observed for low-tannin genotypes compared to tannin-containing genotypes. The protein content was 1.9% higher in low-tannin compared to tannin-containing genotypes. The high estimated heritability for concentrations of seed Mg, Ca, Mn, potassium (K), sulphur (S), and protein content in this species suggests that genetic improvement is possible for mineral elements.
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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) | 0.000 | 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".