Final report of the SIM.QM-S7 supplementary comparison, trace metals in drinking water
Bibliographic record
Abstract
SIM.QM-S7 was performed to assess the analytical capabilities of National Metrology Institutes (NMIs) and Designated Institutes (DIs) of SIM members (or other regions) for the accurate determination of trace metals in drinking water. The study was proposed by the coordinating laboratories National Research Council Canada (NRC) and Centro Nacional de Metrologia (CENAM) as an activity of Inorganic Analysis Working Group (IAWG) of Consultative Committee for Amount of Substance - Metrology in Chemistry and Biology (CCQM). Participants included 16 NMIs/DIs from 15 countries. No measurement method was prescribed by the coordinating laboratories. Therefore, NMIs used measurement methods of their choice. However, the majority of NMIs/DIs used ICP-MS. This SIM.QM-S7 Supplementary Comparison provides NMIs/DIs with the needed evidence for CMC claims for trace elements in fresh waters and similar matrices. Main text To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/ . The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.449 | 0.222 |
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".