Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits
Bibliographic record
Abstract
There is increasing acceptance of the presence of variable magmatic contributions to the mineralizing fluids in the formation of volcanogenic massive sulfide (VMS) deposits. The world-class Windy Craggy Cu-Co-Au deposit (>300 MT @ 2.12 wt% Cu) in northwestern British Columbia is of interest because, unlike most VMS deposits, fluid inclusions in quartz from within the deposit range from relatively low to intermediate salinity (most 6–16 wt% equivalent). In this study we used an excimer (193 nm) laser ablation system interfaced to a quadrupole inductively coupled plasma mass spectrometer to quantify key metals and metalloids that are considered by many to be indicative of magmatic contributions to hydrothermal ore deposits. Although LA-ICP-MS signals from these low-salinity inclusions are highly transient, we were able to quantify Na, Mg, K, Ca, Mn, Fe, Co, Cu, Zn, Sr, Sn, Ba, Ce, Pb and Bi consistently – of the 34 elements that were monitored. Furthermore, Cl, Sb, Cd, Mo, Rb, Br and As were also measured in a significant number of inclusions. Comparison of the fluid inclusion chemistry with unaltered and altered mafic volcanic and sedimentary rocks and mineralized samples from the deposit indicate that enrichment in the main ore metals (Cu, Zn, Fe, Pb) in the inclusions reflects that of the altered rocks and sulfides. Metals and metalloids that may indicate a magmatic contribution typically show much greater enrichments in the fluid inclusions over the host rocks at the same Cu concentration; in particular Bi, Sn and Sb are significantly elevated when compared to the host rock samples. These data are consistent with the ore-forming fluids at Windy Craggy having a strong magmatic contribution. Supplementary material: fluid inclusion data for temperature of homogenization and salinity, and full analytical results for laser ablation ICP-MS analyses of individual inclusions for the two analytical sessions are available at https://doi.org/10.6084/m9.figshare.c.5443094
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".