Performance of commercial laboratories in analysis of geochemical samples for gold and the platinum group elements
Why this work is in the frame
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Bibliographic record
Abstract
Twenty-six international geological certified reference materials (CRM) and two in-house soil control samples were gathered and submitted ‘blind’ in duplicate to five commercial geochemical laboratories for the determination of Au and the platinum group elements (PGEs). The methods employed comprise: Pb fire assay (PbFA) combined with inductively coupled plasma mass spectrometry (ICP-MS); NiS fire assay combined with ICP-MS or instrumental neutron activation analysis (INAA); and aqua regia ICP-MS, with and without prior roasting of the sample at 600 o C. The CRMs vary widely in their matrix and PGE concentrations, ranging from a background soil (e.g. GPt-1), sediment (e.g. GPt-2, JSd-2), and rock (e.g. WGB-1, CHR-Bkg) to altered rocks (e.g. WPR-1) and ore material (e.g. GPt-6, SARM-7b, WMS-1). The results of this ‘round-robin’ are provided and discussed in this paper. Results for Au showed the greatest variation across the laboratories, with one evidently encountering significant and spurious contamination. Comparison of the two fire assay techniques for Au, Pt and Pd was difficult as the number of data points was low and the variance within each technique across laboratories was too high. In general, PbFA-based methods for Au, Pt and Pd produced more accurate and precise results than those by NiS fusion and the data support PbFA detection limits for a 5–10 g sample of 1, 0.1 and 0.5 ppb for Au, Pt and Pd, respectively. A PbFA dataset for Rh demonstrated that this element is not recovered efficiently using an Ag inquart. Measurement of Rh by INAA rather than ICP-MS following NiS fusion facilitates detection below 1 ppb to c. 0.1–0.2 ppb. NiS/ICP-MS results for Ru, Os and Ir support detection limits of 1–2, 2–3 and 0.1 ppb, respectively; mean precision for these elements is in the range 10–15% RSD. Recovery of Os was very low by one laboratory, probably caused by its volatilization as OsO 4 during final digestion in the NiS procedure. As expected, recovery of the analytes by aqua regia was low and highly variable across the different matrices for Pt, Ru, Os and Ir but that for Au and Pd was often >80%; prior roasting of the samples had mixed effects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it