Geochemical characterization of the Central Mineral Belt U ± Cu ± Mo ± V mineralization, Labrador, Canada: Application of unsupervised machine-learning for evaluation of IOCG and affiliated mineral potential
Bibliographic record
Abstract
The Central Mineral Belt (CMB) in Labrador, Canada, hosts multiple U (±base ± precious metal) showings, prospects and deposits in metamorphosed and variably hydrothermally altered Neoarchean to Mesoproterozoic, igneous and sedimentary rocks. Previous work has recognized U mineralization locally associated with Fe-Ca and alkali metasomatism typical of metasomatic iron oxide and alkali-calcic alteration systems (IOAA) that host iron oxide-copper-gold (IOCG) and affiliated critical metal deposits. However, the type, extent and temporal or genetic relationships between the diverse Fe, Ca and alkali metasomatism and the regionally distributed U mineralization remains poorly understood. Combined unsupervised machine-learning and classification of alteration from a large geochemical dataset distinguish the main alteration phases in the CMB, identify compositional changes related to U mineralization, and infer lithological/mineralogical information from samples with censored (i.e., missing), limited and/or inaccurate metadata. Weak to intense Na and Na + Ca-Fe (Mg) metasomatism in the southwest (Two-Time and Moran Lake areas) and eastern (Michelin area) portions of the CMB pre-dates U mineralization and Fe-oxide breccia development, similar to albitite-hosted U and IOCG deposits globally. Rare earth elements and spider diagrams highlight both preservation and disruption of normally immobile elements. Principal component and cluster analysis indicate significant variations in Fe-Mg ± Na contents in the rocks from combinations of Na, Ca, Fe, and Mg-rich alteration, while protolith REE signatures can be locally preserved even after pervasive albitization-hematization. Cluster analysis identifies mineralized felsic and mafic rocks in the Michelin deposit and Moran Lake area, facilitating inference of relevant lithological/mineralogical information from samples lacking or with limited meta-data. The methods outlined provide rapid and relatively inexpensive means to optimize identification of mineral systems within large geochemical datasets, verify drill core or field observations, highlight potentially overlooked alteration, and refine economic mineral potential assessments. Based on our results and previous work, we suggest the mineral potential of the southwestern and eastern CMB needs to be re-assessed with modern exploration models for IOAA ore systems and their iron oxide-poor variants.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".