Continuous leach inductively coupled plasma mass spectrometry: applications for exploration and environmental geochemistry
Bibliographic record
Abstract
Continuous leach inductively coupled plasma mass spectrometry (CL-ICP-MS) is a new analytical technique developed to address some of the shortcomings of bulk leach techniques while overcoming the uncertainties associated with interpreting selective extraction data. It provides information on the specific geochemical sites and mineral phases from which elements are being released using real-time data generated by continually analysing progressively reactive solutions from water to 30% nitric acid as they are pumped through a sample directly into a high resolution ICP mass spectrometer. Mineral breakdown reactions can be monitored from the major elements released, thereby eliminating uncertainties related to host phase/trace element associations. By comparing major and minor element release patterns, trace elements can be reliably assigned to host phases. Results from single mineral phases, mixtures of mineral phases, and natural ore samples indicate that the release of elements from specific minerals is not obscured in more complex samples and that reprecipitation and back reactions are not a concern with this method. Scanning electron micrograph (SEM) examination of the reaction products has been used to verify which phases react and to support the CL-ICP-MS data interpretation. Results for natural soil samples indicate that ‘false’ mobile element anomalies can be identified using CL-ICP-MS and underscore the importance of understanding where trace elements reside in samples used for environmental studies or mineral exploration.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".