Trace Element Composition of Chalcopyrite from Volcanogenic Massive Sulfide Deposits: Variation and Implications for Provenance Recognition
Bibliographic record
Abstract
Abstract Chalcopyrite from 51 volcanogenic massive sulfide (VMS) and sea-floor massive sulfide (SMS) deposits from six lithostratigraphic settings was analyzed for trace elements by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to evaluate its potential as an indicator mineral for exploration. Partial least squares discriminant analysis (PLS-DA) results reveal that chalcopyrite from different lithostratigraphic settings has different compositions reflecting host-rock assemblages and fluid composition. Three random forest (RF) classifiers were developed to distinguish chalcopyrite from the six lithostratigraphic settings with a divisive approach. This method, which primarily classifies according to the major host-rock affinity and subsequently according to VMS settings, yielded an overall accuracy higher than 0.96 on test data. The model validation with literature data having the same elements required by the models yielded the highest accuracies (>0.90). In validation using published data with missing elements, the accuracy is moderate to high (0.60–1); however, the performances decrease significantly (<0.50) when the most important elements are missing. Similarly, RF regression models developed using all sets of analyzed elements to determine ccp/(ccp + sp) ratio (ccp = chalcopyrite; sp = sphalerite) in chalcopyrite within a single VMS setting reported high performances, thus showing a potential to predict the Cu/Zn ratio (Cu-rich vs. Zn-rich) of the mineralization based on chalcopyrite composition. This study demonstrates that trace element concentrations in chalcopyrite are primarily controlled by lithotectonic setting and can be used as predictors in an RF classifier to distinguish the different VMS subtypes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".