The direct and accurate determination of major elements Ca, K, Mg and Na in water by HR-ICPMS
Bibliographic record
Abstract
Abstract A direct, accurate and precise method is reported for major elements Ca, K, Mg and Na measurements in river and drinking water using a high resolution ICP-MS. Double isotope dilution calibration was used for the determination of Mg whereas the combined standard addition and internal standardization (Sc) was used for Ca, K and Na measurements. The method is validated by analysis fresh water SLRS-5, SLRS-6 and SRM1640a with satisfactory results characterized by high precisions of 0.055% to 0.66% RSD (or 0.29–1.8% relative combined uncertainty) for all four analytes, superior to those reported in earlier studies. As noted, use of internal standard Sc has significantly (3–33 times) improved measurement precisions for Ca, K and Na compared to standard addition calibration alone. The proposed method was applied for the determination of major elements Ca, K, Mg and Na in a candidate drinking water CRM AQUA-1. Values of 1.908 ± 0.007 µg g −1 , 8.27 ± 0.14 µg g −1 , 0.660 ± 0.010 µg g −1 and 13.76 ± 0.05 µg g −1 ( u , k = 1) were obtained for Mg, Ca, K and Na in AQUA-1 drinking water, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".