Comparison of methods for the geochemical determination of rare earth elements: Rock Canyon Creek REE–F–Ba deposit case study, SE British Columbia, Canada
Bibliographic record
Abstract
Using the Rock Canyon Creek carbonate-hosted rare earth element (REE)–F–Ba deposit as an example, we demonstrate the need for verifying inherited geochemical data prior to reinterpretation. Inherited La, Ce, Nd and Sm data obtained by pressed pellet X-ray fluorescence (XRF), and La and Y data obtained by aqua regia digestion inductively coupled plasma atomic emission spectroscopy (ICP-AES) for more than 300 drill-core samples were analysed in 2009 and were subsequently compared to sample subsets re-analysed using lithium metaborate-tetraborate (LMB) fusion ICP mass spectroscopy (ICP-MS), Na 2 O 2 fusion ICP-MS, and LMB fusion-XRF. We determine that LMB ICP-MS and Na 2 O 2 ICP-MS accurately determined REE concentrations in control reference materials (CRM) SY-2 and SY-4, and provided precision of about 10%. Fusion-XRF was precise for La, Ce and Nd at concentrations greater than ten times the lower detection limit; however, accuracy of this method was not established because REE concentrations in SY-4 were below the lower detection limit. Analysis of the sample subset revealed substantial discrepancies for Ce concentrations determined by pressed pellet XRF in comparison to those determined by other methods due to Ba spectral interference. Samarium, present in lower concentrations than other REE that were determined, was consistently underestimated by XRF methods relative to ICP-MS methods. This may be the result of Sm concentrations approaching the lower detection limits of XRF methods, elemental interference or inadequate background corrections. Aqua regia dissolution results, reporting only for La and Y, are underestimated relative to the other methods. We highlight the importance of selecting the most appropriate analytical method and reference materials for determining the REE content of mineralized rock which may be several orders of magnitude higher than that of typical host rock.
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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 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".