Chlorite-White Mica Pairs’ Composition as a Micro-Chemical Guide to Fingerprint Massive Sulfide Deposits of the Bathurst Mining Camp, Canada
Bibliographic record
Abstract
The compositions of phyllosilicates, with a focus on fluid-mobile elements, were evaluated as a means to fingerprint the Middle Ordovician metamorphosed (greenschist facies) volcanogenic massive sulfide deposits of the Bathurst Mining Camp (BMC), Canada. Ninety-five drill-core samples from six of the major deposits of the Bathurst Mining Camp (Brunswick No. 12, Heath Steele B zone, Halfmile Lake Deep zone, Key Anacon East zone, Louvicourt, and Restigouche) were analyzed using electron microprobe and laser ablation inductively coupled plasma-mass spectrometry. Typically, phyllosilicates (chlorite, white mica, and to a lesser extent biotite) are ubiquitous phases in the host rocks of the massive sulfide deposits of the BMC. Electron microprobe analysis results show a wide compositional variation in chlorite and white mica. Laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis was performed to measure fluid-mobile elements, showing that Tl is distinctly enriched in all white mica (up to 719 ppm) relative to chlorite (up to 50.1 ppm). Chlorite hosts Sn (up to 4600 ppm), Hg (up to 7.3 ppm), Sb (up to 35.4 ppm), As (up to 1320 ppm), In (up to 307 ppm), Cd (up to 83.2 ppm), and Se (up to 606 ppm). White mica hosts Sn (up to 1316 ppm), Hg (up to 93 ppm), Sb (up to 1630 ppm), As (up to 14,800 ppm), In (up to 1186 ppm), Cd (up to 98 ppm), and Se (up to 38.8 ppm). Limited LA-ICP-MS analysis on biotite indicates a higher overall concentration of Tl (mean = 14.6 ppm) relative to co-existing white mica (mean = 2.18 ppm). On average, biotite is also more enriched in Hg, Sn, and Ba relative to chlorite and white mica. Laser Ablation ICP-MS profiles of chlorite, white mica, and biotite demonstrate smooth time-dependent variations diagnostic of structural substitution of these elements. Compositional variation of chlorite-white mica pairs presented in the current study shows systematic variations as a function of distance from the mineralized horizons. This highlights the potential to use trace-element signatures in these phyllosilicate pairs to identify proximal (chlorite) and distal (white mica) footprints for volcanogenic massive sulfides 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".