Solid sampling ETV-ICP-OES to study the distribution of elements in clay and soil samples for mineral exploration
Bibliographic record
Abstract
A simple and fast method, using solid sampling electrothermal vapourization inductively coupled plasma optical emission spectrometry (ETV-ICP-OES), was developed to determine the distribution of elements in clay separates and soil samples from across the Talbot Lake VMS Cu-Zn prospect, in the Flin Flon-Snow Lake terrane, Manitoba, Canada in order to locate the undercover ore deposit, which is buried under Palaeozoic dolomites and Quaternary till. In the development of the method, the mass of sample, the mass of carrier agent (polytetrafluoroethylene, PTFE) or flow rate of reactant gas (dichlorodifluoromethane (R12)), the carrier and bypass gas flow rates and the temperature program were optimised. Under optimal conditions and with a four-step ETV temperature program, the distribution of the pathfinder elements (Zn, P, S and I) in clay separates and soils showed clear anomalies at 400 and 650 m. The results for Zn and P are in very good agreement with results obtained, following aqua regia (AR) digestion, by ICP mass spectrometry (ICP-MS) by Anglo American Exploration Division (AA-ED). Moreover, the distributions of S and I could be precisely determined (these elements were not reported in the AA-ED study). Using 0–4 mg of AA-ED S5 standard mixed with 2 mg PTFE or with 4.1 ml/min R12 as reactant gas, and using internal standardisation with an argon emission line, calibration curves were obtained that, when applied to Talbot clay separates and soil samples, yielded Zn, S and P concentrations in agreement with AR-ICP-MS results previously obtained by AA-ED. Hence, ETV-ICP-OES completely eliminates the need for clay separation and for extraction or digestion of samples prior to analysis, which significantly simplifies the analysis of geochemical exploration samples.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".