Evaluating Geochemical Discriminants in Archean Gold Deposits: A Superior Province Perspective with an Emphasis on the Abitibi Greenstone Belt
Bibliographic record
Abstract
Abstract Discriminating Archean Au deposit types and related ore-forming processes is challenging but paramount for increasing Au exploration success. This study tests the validity of applying geochemical data generated from conventional bulk versus modern in situ methods as discriminants for classifying Au deposits in the Archean Swayze greenstone belt with further comparison to other deposits in the contiguous Abitibi greenstone belt and Red Lake area (Superior Province, Canada). The study used five well-characterized Au settings, based on new mapping, as a basis for evaluating in situ (δ18Oquartz, δ33, 34Ssulfide, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite, electron microprobe analysis of gold), and whole-rock geochemical datasets to resolve whether intrusion-related Au deposits can be discriminated from orogenic-type Au deposits. Results show that the in situ methods provide insight into processes related to Au mineralization, both primary and subsequent remobilization and upgrading, and define elemental and isotopic correlations that cannot be resolved using conventional bulk methods. For example, when comparing the whole-rock to laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) trace metal data, only Ag, Te, and Hg show a consistent positive correlation with Au across deposit types in both data sets. Furthermore, the wholerock datasets combined with in situ isotopic analysis suggest the Archean sanukitoid-associated Au deposits represent a distinct group of intrusion-related deposits with mineralization characterized by low δ34Spyrite (<–5 to –25‰), inferred high fO2, an Hg-Te signature, and hosted in intrusions of <2690 Ma that predate shearing. The data and interpretations presented herein provide a baseline that can be widely utilized in future studies of Au deposits.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".