Current Techniques and Applications of Mineral Chemistry to Mineral Exploration; Examples from Glaciated Terrain: A Review
Bibliographic record
Abstract
This paper provides a summary of traditional, current, and developing exploration techniques using indicator minerals derived from glacial sediments, with a focus on Canadian case studies. The 0.25 to 2.0 mm fraction of heavy mineral concentrates (HMC) from surficial sediments is typically used for indicator mineral surveys, with the finer (0.25–0.50 mm) fraction used as the default grain size for heavy mineral concentrate studies due to the ease of concentration and separation and subsequent mineralogical identification. Similarly, commonly used indicator minerals (e.g., Kimberlite Indicator Minerals—KIMs) are well known because of ease of optical identification and their ability to survive glacial transport. Herein, we review the last 15 years of the rapidly growing application of Automated Mineralogy (e.g., MLA, QEMSCAN, TIMA, etc) to indicator mineral studies of several ore deposit types, including Ni-Cu-PGE, Volcanogenic Massive Sulfides, and a variety of porphyry systems and glacial sediments down ice of these deposits. These studies have expanded the indicator mineral species that can be applied to mineral exploration and decreased the size of the grains examined down to ~10 microns. Chemical and isotopic fertility indexes developed for bedrock can now be applied to indicator mineral grains in glacial sediments and these methods will influence the next generation of indicator mineral studies.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".