Variability in mineral composition of Canadian lentil genotypes
Bibliographic record
Abstract
Lentils are a good dietary source of essential minerals for the proper functioning of the human body. We evaluated 34 cultivars and elite lentil lines representing the breadth of the Canadian breeding program. Trials were established in 10 site-years across Saskatchewan. Concentrations of 27 minerals were quantified with an inductive coupled argon plasma emission spectrometer in whole and dehulled lentil seeds. Lithium (Li), vanadium (V), chromium (Cr), cobalt (Co), arsenic (As), silver (Ag), cadmium (Cd), tin (Sn), lanthanum (La), mercury (Hg), and lead (Pb) had concentrations below the quantification limit and were excluded from further analysis. The effects of site year, tissue type (whole and dehulled), and lentil genotypes were analyzed using a mixed model. Mineral concentrations of iron (Fe), zinc (Zn), magnesium (Mg), sodium (Na), potassium (K), phosphorus (P), sulfur (S), manganese (Mn), copper (Cu), molybdenum (Mo), boron (B), nickel (Ni), selenium (Se), aluminum (Al), and barium (Ba) was different among genotypes and across environments. Most minerals except Cu had significantly different concentrations between whole and dehulled seeds. Ca, Fe, Mg, Mn, B, Al, and Ba were more concentrated in whole seeds, while K, S, and Ni were higher in dehulled seeds. Among the 34 genotypes, CDC Impala had the highest Fe, Zn, K, P, S, and Cu concentrations. Lentil genotypes with a higher composition of several minerals could be a starting point for enhancing mineral composition in lentils.
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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 | 0.001 | 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".