Trace Element Data for Gold, Iridium and Silver in Seventy Geochemical Reference Materials
Bibliographic record
Abstract
New concentrations for Au, Ir and Ag obtained by instrumental neutron activation analysis are presented for seventy geochemical reference materials. Results in agreement with literature values for Au and Ir down to concentrations of a few ng g−1 were obtained. For Au and Ir concentrations above 10 ng g−1, the repeatability of replicate analyses of reference materials was mostly better than 10%. For concentrations between 1 and 10 ng g−1 the RSD for Ir was 10–30%, whereas for Au it was higher and more variable (20–50%). In addition, concentrations for Cd and Hg are presented for some of the same reference materials. The high RSD at relatively high concentrations seen in gold for some RMs (e.g., WMG‐1, WMS‐1) did not exist for Ir and suggests homogeneity for this platinum‐group element at the sub‐sample size used in this study. For the following eight RMs, mostly ultramafic rocks (CHR‐Pt+, OREAS‐13P, OREAS‐14P, PCC‐1, UMT‐1, WMG‐1, WMS‐1, WPR‐1), Ir measurements agreed within ± 10% of mostly certified or recommended concentrations, which ranged from 2 ng g−1 to 6 μg g−1. For the reference material UB‐N, iridium concentration compared favourably to published results obtained by isotope dilution ICP‐MS methods and a previously unrecognised heterogeneity is inferred for Au, Hg and Sb, but not for the other measured elements.
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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 | 0.006 | 0.003 |
| 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) | 0.003 | 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".