Chemical quality of bottled mineral waters from markets of Curitiba-PR-Brazil
Bibliographic record
Abstract
Twenty-seven bottled mineral waters from local markets in Curitiba, State of Paraná, Brazil were analyzed for a number of constituents, including: pH, conductivity, total dissolved solids, hardness, HCO3 - and CO 3 2-, as well as Ca2+, Mg2+, Na+, K+, NH4 + , F-, Cl-, NO3 -, NO2 - and SO4 2- by ion chromatography (IC); Al, Ba, Cd, Cr, Cu, Fe, Li, Mn, Ni, Sr, V and Zn by inductively coupled plasma optical emission spectrometry (ICP OES); Hg by cold vapor generation-ICP OES and As, Pb, Sb and Se by ICP-mass spectrometry (ICP-MS). The obtained limits of quantitation (LOQ) were adequate for the determination of all analytes using the various analytical techniques. Results for the analysis of AccuStandard certified reference materials QCS-01-5, QCS-02-R1-5 and TCLP-02-1 as well as spike recoveries to samples show acceptable values, within 90-111 % of expected concentrations with relative standard deviations below 10 %, demonstrating the accuracy of the determinations. Both NO2 - and NO 3 - were above the maximum limits set by Brazilian legislation in two samples, indicating a microbiological contamination. One imported sample presented As (6.1 ± 0.2 µg L-1) near the maximum limit (10 µg L-1) while all other elements of interest were below the values specified by Brazilian legislation. Principal component analysis revealed that four imported samples and one from the Minas Gerais State have the highest mineral concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".