Mineral Contents of Chickpea Cultivars (Cicer arietinum L.) Grown at Different Locations of Turkey
Bibliographic record
Abstract
Mineral deficiency-induced diseases and health problems influence billions of people worldwide.Development of edible legumes with high mineral contents will provide significant contributions in reducing the frequency of such diseases.Among the edible legumes, chickpea has quite high nutritional values and quite low production costs, thus commonly grown and consumed worldwide.This study was conducted to determine mineral (potassium, phosphorus, sulphur, calcium, magnesium, sodium, zinc, iron, boron, manganese, copper) contents of eight different chickpea cultivars (Akçin-91, Azkan, Hisar, Akça, Gökçe, Yaşa-05, Işık-05 and Çakır) grown in two different locations (Afyonkarahisar and Yozgat provinces of Turkey) for two years (2015 and 2016).Experiments were conducted in randomized blocks design with three replications.Among the minerals, potassium was highest (6435.2-8231.7 mg kg -) followed by phosphorus (2573.9-3094.0mg kg -1 ) and sulphur (1710.9-2060.7 mg kg -1 ).Among the minerals, copper was lowest followed by boron and manganese.Considering the average of locations, it was observed that Gökçe, Akçin-91, Azkan and Hisar cultivars were prominent with sulphur content; Akça with boron, sodium and calcium contents; Yaşa-05 and Işık-05 with potassium, phosphorus, magnesium, zinc, iron, magnesium, manganese and copper contents.Çakır cultivar was stable for all minerals.The concentrations of seed P, S, Ca, Mg, B and Mn were significantly affected by locations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".