Advancing exploration hydrogeochemistry using single particle inductively coupled plasma–time-of-flight mass spectrometry at the Bear Lodge alkaline complex, Wyoming, USA
Bibliographic record
Abstract
In this study, we demonstrate the use of single-particle inductively coupled plasma–mass spectrometry (spICP–MS) as a new tool for exploration hydrogeochemistry through a case study at the Bear Lodge alkaline complex in Wyoming, USA. Nanoparticulate forms of gold and associated pathfinder elements (Ag, Sb, Bi, Tl and Te) were detected in well waters proximal to rare earth element (REE) and Au mineralization, resulting in stronger geochemical anomalies compared with conventional acidified water analysis. Using the multi-elemental capability of spICP–time-of-flight MS (spICP–ToF-MS) to analyse waters, we detected REE particles that were hypothesized to be nanoscale REE mineral grains originating from the oxidized zone of carbonatite mineralization. For the first time, we present chondrite-normalized REE patterns from individual nanoparticles, allowing for geochemical interpretations based on light rare earth element enrichment and Ce anomalies. This study demonstrates that spICP–MS analysis of waters could be a valuable tool to detect concealed mineralization in environments with weak hydrogeochemical signatures. Moreover, the detection of indicator minerals using spICP–ToF-MS represents a novel use of the technique that may provide additional, previously unattainable information from water samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".