Phytic acid and mineral micronutrients in field-grown chickpea (Cicer arietinum L.) cultivars from western Canada
Bibliographic record
Abstract
Zinc (Zn), iron (Fe), magnesium (Mg), and calcium (Ca) in chickpea seed are important constituents in vegetarian diets. The aim was to investigate associations of these nutrients in different chickpea (Cicer arietinum L.) cultivars with phytic acid (PA), another naturally occurring constituent of grain that may influence the bioavailability of mineral micronutrients. Chickpea was grown at Saskatoon and Swift Current, SK, in 2002 and 2003, representing dryland production from high-yielding locations in western Canada. Minerals were measured by atomic absorption spectroscopy; PA was measured using high-performance anion-exchange conductivity detection methodology. Seed from 10 genotypes contained from 29 to 52 mg/kg Zn, 77–112 mg/kg Fe, 1,448–2,457 mg/kg Mg, 1,211–2,457 mg/kg Ca, to 3.8–9.0 mg/g PA. Phytic acid, Fe, Mg, and Ca decreased in 2003 from 2002 concentrations. Kabulis had greater Zn, the same Fe, but lower Mg and Ca concentrations than desi genotypes. Large-seeded genotypes had greater or the same Zn, the same Fe and Mg, but lower Ca than small-seeded genotypes. Iron and Ca concentrations positively correlated with PA concentration. Nutrients were affected by environment and genotype, which means that chickpea can be exploited by breeding, in addition to sourcing favorable nutritional profiles by environment, seed size, and market class.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".