Mineral Concentrations in Bottled Water Products: Implications for Canadians’ Mineral Intakes
Bibliographic record
Abstract
PURPOSE: The popularity of bottled water products (BWPs) is growing in Canada. Concentrations of minerals with important implications for health were compared in different types of BWPs. METHODS: One sample of each brand and type of plain BWP (purified, remineralized, spring, mineral, and artesian), flavoured BWP, and nutrient-enriched BWP sold in major stores in Ottawa, Ontario, was purchased to allow determination of mineral concentrations by flame atomic absorption or emission spectroscopy. A total of 124 BWPs representing 37 brands were analyzed. RESULTS: In general, spring and mineral water contained higher amounts of magnesium and calcium than did purified, remineralized, artesian, flavoured, or nutrient-enriched water. Most plain BWPs contained little sodium and potassium, whereas 15% to 35% of flavoured and nutrient-enriched products had considerably higher concentrations. Only magnesium and calcium concentrations were highly correlated (r=0.76, p<0.001). Calculation of the percentage of Dietary Reference Intakes that could be supplied by each product revealed that, if they are consumed habitually, many products can contribute substantially to recommended intakes of these minerals. CONCLUSIONS: Mineral concentrations in most types of BWP varied, but distinct differences between types of products were identified. Consumers should be aware of the mineral content of BWPs because some could influence intakes of certain minerals significantly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".