Indicators of gold mineralization in the Yellowknife greenstone belt: a lithogeochemistry and mineralogy study
Bibliographic record
Abstract
Geochemical signatures within an economic deposit are an important indicator used in the exploration of high-grade mineralization. In this study, we aim to identify elements that can act as geochemical indicators for orogenic gold within the Yellowknife greenstone belt that allow the discrimination of mineralized structures. The ability of a portable x-ray fluorescence spectrometer (pXRF) to provide a fast and accurate assessment of compositions depends on particle sizes and the degree of chemical homogeneity within various lithologies. Therefore, we have tested the precision and accuracy of pXRF in six drill cores with heterogeneous mineralogy, lithology and grain sizes from various locations across the Yellowknife Greenstone Belt against standard assay data and inductively coupled plasma mass spectrometry (ICP) data from Gold Terra. The six cores come from three drilling locations within Gold Terra’s mineral claims and represent gold mineralization hosted within two different lithologies. The pXRF analysis picks up distinct geochemical trends approaching mineralized structures and surrounding alteration/shear zones; However, the trends vary by location and host rock lithology. The correlation coefficients for elements analyzed with respect to gold were calculated at a belt wide scale for all drilling locations, and at a local scale for each drilling location. The elements with the highest correlation with gold across all drilling locations were As and S and vary at a local scale, dependent on host-rock lithology and alteration. Mineralogical variability as a function of distance from gold-bearing structures has been determined to assess what minerals control the geochemical trends detected. Oxide minerals such as rutile, titanite, and ilmenite are the mineralogical controls for geochemical trends seen in siderophile elements, while chlorite is the mineralogical control for lithophile elements, such as magnesium. Across lithologies, ilmenite (FeTiO3) displays replacement textures as it is altered to rutile (TiO2) and titanite (CaTiSiO5), liberating iron in the process. These iron liberating replacement reactions likely contributed to the iron budget that aided in the sulphidation of the wall rock and destruction of the gold bisulphide complex, triggering gold deposition.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".