Comparison of the nutrient content of commercially purchased medium seed brown lentils with the world’s leading database
Bibliographic record
Abstract
Abstract The purpose of our study was to ensure that comparing the mineral content of the lentil and the amount of nutrients published by the world's leading organizations. The samples were randomly and subjectively selected from different retail outlets. Fifteen types of medium seed brown lentil from fifteen different distributors were obtained and analyzed for moisture, protein, Na, K, Ca, Mg, P, Fe, Cu, Zn, Mn, and S content. Descriptive statistics were done and for comparisons. Shapiro–Wilk test was first conducted to assess normality. When data followed a normal distribution, T-test was used, and when not, Wilcoxon signed rank test (P-values = 0.05). The results of the measurements were compared with data from several FAO/INFOODS food composition databases, as well as the Canadian National Food Composition Database, USDA Food Data Central, United Kingdom, Australian Food Composition Database, and Indian food composition tables. The evaluation of the measurement results showed significant differences (p = 0.05) in the amount of Na, K, Ca, Mg, P, Fe, and Cu compared to the amounts listed in the world's leading databases in most cases. Our results were also examined from a dietary perspective to determine if the differences had practical significance. The results of the Canadian samples were compared with the Canadian database, there was a significant difference amount of Na, K, Ca, Mg, P, Fe, Cu, and Mn. For each discrepancy, more than the quantitative values published in the databases were measured, in the case of Ca, Mg, and Fe almost double.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 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".