A comparison of the capacity of FA-ICP-MS and FA-INAA to determine platinum-group elements and gold in geological samples
Bibliographic record
Abstract
The platinum-group elements (PGEs) are commonly determined by INAA and ICP-MS after a NiS fire assay preconcentration. The results of the initial “round robin” for the PGEs and gold were examined for geological Canadian reference materials (WGB-1, TDB-1, UMT-1, WPR-1, WMG-1, and WMS-1). The Au accuracy is generally within 15% for both methods. For Ir, Os, Pd, Pt and Rh the accuracy for most samples is better than 10% for FA-ICP-MS and FA-INAA (true only for sulfide-bearing samples in the case of FA-INAA). Ru is not very accurate by either methods. Ru and Au have problems with precision which is interpreted to be related to the loss of gold in the dissolution step and for Ru, the source of the problem is not yet understood. Kurtosis show that FA-INAA has higher clustering than FA-ICP-MS for most analytes. It suggests a slightly better precision for FA-INAA. This is explained by the robustness of INAA after the NiS preconcentration despite its lower instrumental precision versus the complex dissolution steps involved in ICP-MS. For samples richer in PGEs (sulfide- and/or oxide-bearing rocks) both methods perform adequately but for low PGEs concentration samples (crustal rocks) ICP-MS shows an advantage.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".