Comparison between Nickel‐Sulfur Fire Assay Te Co‐precipitation and Isotope Dilution with High‐Pressure Asher Acid Digestion for the Determination of Platinum‐Group Elements, Rhenium and Gold
Bibliographic record
Abstract
The accurate determination of platinum‐group elements (PGE), rhenium and gold is important in both fundamental research and ore deposit studies. Questions have been raised by some authors as to whether the nickel‐sulfur fire assay technique (NiS‐FA) efficiently collects all the PGE. On the other hand, most isotope dilution (ID) techniques can only treat small test portion masses (2 g was used for high‐pressure asher digestion; HPA) and this makes them more vulnerable to nugget effects. We determined PGE concentrations in ten reference materials with the aim of comparing the performance characteristics of the two methods. Both methods determine Ru, Pd, Os, Ir and Pt and we found that there were no significant systematic differences in the recovery. The advantages of NiS‐FA were that: (a) it is capable of determining Rh and Au; (b) the relatively large test portion mass (15 g) reduces the nugget effect and (c) it is faster and less expensive than HPA‐ID. The advantages of HPA‐ID were that: (a) it determined Re and (b) it had low level blanks, lower detection limits and hence better precision in low‐level homogeneous samples. Each technique had advantages and limitations; they should be considered as complementary rather than competing techniques.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".