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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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".