SLRS‐5 Elemental Concentrations of Thirty‐Three Uncertified Elements Deduced from SLRS‐5/SLRS‐4 Ratios
Bibliographic record
Abstract
The fifth version of natural river water certified reference material, SLRS‐5 (National Research Council – Conseil National de Recherches Canada), is commonly used to control the quality of major and trace element measurements. Concentrations of silicon and thirty‐one uncertified trace elements have been reported for the certified reference material SLRS‐4, but they are not yet available for SLRS‐5. Here, SLRS‐5/SLRS‐4 ratios were deduced from SLRS‐5 and SLRS‐4 measurements by inductively coupled plasma‐atomic emission spectrometry and high‐resolution inductively coupled plasma‐mass spectrometry for certified elements and thirty‐five uncertified elements (rare earth elements, B, Bi, Br, Cs, Ga, Ge, Hf, Li, Nb, P, Pd, Rb, Rh, S, Sc, Si, Sn, Th, Ti, Tl, Y). Both reference materials were measured directly one after the other, so that calculated elemental ratios would not be notably influenced either by calibration uncertainties or by eventual long‐term instrumental drift. The computed ratios are in good agreement with those deduced from the certified values. We also report concentrations for thirty‐three uncertified elements in SLRS‐5 by combining the measured SLRS‐5/SLRS‐4 ratios and the published SLRS‐4 values. The resulting new data set provides target SLRS‐5 values, which will be useful in quality control procedures.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".