Analysis of Minerals in Foods: A Three-year Survey from Costa Rican Market Products
Bibliographic record
Abstract
Developing and carrying out analyzes that allow nutritional profiling of foods has become increasingly necessary in the food industry, especially when essential nutrients, such as minerals, are involved. In addition, having this type of information makes it possible to characterize the food, corroborate labeling, monitor regulations, improve food quality, and take public health measures when there are deficiencies or excesses in the population level of any nutrient. During this survey, total ash, Cl, Ca, P, Mg, Fe, Zn, Cu, Na, and K, were analyzed in different foods (including meat, dairy, cocoa, baked products, fruits, vegetables, legumes, beverages, cocoa products), for a total of n = 2046, 190, 385, 101, 113, 718, 190, 79, 945, and 190 samples, respectively. These samples were compiled from January 2019 to December 2021 as part of routine surveillance of the food industry. Food mineral fraction was assessed by gravimetry, chloride by potentiometry, and the rest of the analytes by spectrometry. Descriptive statistics were produced to analyze the database, and the information was divided by type of food and minerals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".