MAJOR- AND TRACE-ELEMENT ANALYSIS OF SULFIDE ORES BY LASER-ABLATION ICP MS, SOLUTION ICP MS, AND XRF: NEW DATA ON INTERNATIONAL REFERENCE MATERIALS
Bibliographic record
Abstract
International reference materials representing a diverse suite of sulfide ores and related lithologies have been analyzed for major and trace element concentrations by XRF, solution ICP-MS, and laser-ablation ICP-MS (LA-ICP-MS) after fusion of the sample to a lithium borate glass. Reference materials analyzed for this study include a wide variety of bulk compositions, including ores of Pb-Zn sulfides, Fe sulfides, Cu-Mo sulfides, and silicate matrices. Concentrations of 33 elements were determined, including lithophile and chalcophile elements of particular interest to economic geochemistry and ore deposit studies. The results of LA-ICP-MS analyses were calibrated using a fused glass standard prepared specifically for the analysis of sulfides. Accuracy of the LA-ICP-MS technique is established by comparison with results obtained by the other methods for abundance variations over several orders of magnitude. Replicate analyses demonstrate a precision of 2-8% (1 RSD) for the LA-ICP-MS data at rock-equivalent concentrations >1 ppm. Matrix effects were not a significant problem at the scale of compositional variation represented by these samples, and no significant differences in the results were produced with the laser operating in either fixed-spot or line-scan mode. LA-ICP-MS analysis of fused glasses effectively overcomes problems related to insoluble phases such as cassiterite that are resistant to acid dissolution. Whole-rock analysis of sulfide ores by XRF and LA-ICP-MS provides a fast and convenient approach for determinations of major-and trace-element concentrations in a variety of ores and related materials without the need for wet-chemical dissolutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".