Trace element signatures in scheelite associated with various deposit types: A tool for mineral targeting
Bibliographic record
Abstract
Scheelite is a widespread mineral in several geological settings and its trace element composition provides valuable information about the source and composition of the hydrothermal fluids. In this study, scheelite from 22 magmatic-hydrothermal deposits and 2 orogenic Au deposits (Hangar Flats and Corcoesto) were analyzed by EPMA and LA-ICP-MS. Magmatic-hydrothermal scheelite, together with literature data are investigated using partial least square-discriminant analysis (PLS-DA) and Random Forest (RF) classifier, to evaluate the use of scheelite as a robust indicator mineral for W-bearing deposit targeting. Cathodoluminescence images show that scheelite is texturally homogeneous in reduced intrusion-related gold systems (RIRGS) and varies from homogeneous to heterogeneous in other magmatic-hydrothermal and orogenic Au deposits. Scheelite displays six REE chondrite-normalized patterns, which are a function of the source and composition (mainly salinity) of the mineralizing fluids and partitioning with co-genetic minerals (e.g., garnet, clinopyroxene). The PLS-DA highlights that scheelite trace element composition from magmatic-hydrothermal deposits varies following different deposit types (e.g., oxidized and reduced skarns, porphyry WMo, RIRGS, quartz-vein/greisen SnW), and that such compositional variation reflects mainly the difference of fO2 and composition of mineralizing fluids. Additionally, scheelite from magmatic-hydrothermal deposits are chemically distinct to those formed dominantly by metamorphic fluids in orogenic settings as shown by their higher Mo, Nb and Mn, and lower Sr contents and predominantly negative Eu anomalies. Metamorphic scheelite can be discriminated from that of orogenic Au deposits by their lower Pb, As and REE contents and LREE/HREE ratios, which are related to local host rock composition and metamorphic grade. Using Na, Mg, Mn, As, Sr, Y, Nb, Mo, Pb, ΣREE concentrations and Eu anomaly as predictors, the RF model yields an overall prediction accuracy of 97 % for test data as function of deposit types (89.2 % for RIRGS, 100 % for porphyry WMo, 97.8 % for quartz-vein/greisen SnW, 96.9 % for oxidized skarn, 98.1 % for reduced skarn and 99.3 % for orogenic Au deposits). Application of RF classifier to scheelite composition from orogenic Au and skarn- and greisen-type W deposits from literature yields an overall prediction of ∼79 % (91 % for oxidized skarn, 71.4 % for quartz-vein/greisen SnW and 74.2 % for orogenic Au deposits) showing that scheelite is an efficient indicator mineral for Au and W deposits targeting. Metamorphic scheelite is predicted mostly as orogenic Au scheelite (83 %), reflecting the genesis of metamorphic fluids and similar geological setting, suggesting that RF classifier can be also used to predict the fluid sources.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".