Indicator mineral methods in mineral exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indicator minerals are mineral species that, when appearing as transported grains in clastic sediments, indicate the presence in bedrock of a specific type of mineralization, hydrothermal alteration or lithology. Their physical and chemical characteristics, including a relatively high density, facilitate their preservation and identification and allow them to be readily recovered at the parts per billion level from sample media such as till, stream sediments or soil producing large exploration targets. Another major advantage of indicator mineral methods is that grain morphology, surface textures or mineral chemistry may be examined to obtain information about transport distance and bedrock source. Indicator minerals have become an important exploration method in the past 20 years and now include suites for detecting a variety of ore deposit types including diamond, gold, Ni–Cu, PGE, porphyry Cu, massive sulphide, and tungsten deposits. One of the most significant events in the application of indicator mineral methods in the past 10 years was the explosion in diamond exploration activity in the glaciated terrain of Canada and the resultant changes in sampling and processing methods and improved understanding of kimberlite indicator minerals. At the same time, technological advances have led to increased sophistication of determining indicator mineral chemistry for all indicator minerals. This paper provides an overview of indicator mineral methods and their application in a variety of terrains in the past 20 years, focusing on gold and diamond exploration.
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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 | 0.001 |
| 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) | 0.001 | 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 it