A review of hydrogeochemical techniques for mineral exploration: history, present and future
Bibliographic record
Abstract
This review provides an overview of the application of hydrogeochemistry in mineral exploration by compiling learnings and knowledge from past and present research. It includes guidance on survey design, sampling methodologies, field data collection, analysis and interpretations. Hydrogeochemistry is a unique tool with the potential to detect and generate anomalies from mineral occurrences that are deeply emplaced or obscured by post-mineral cover. Pathfinder dispersion from mineral deposits commonly produces proximal and distal dispersion patterns; case studies have demonstrated anomalies detectable up to 10 km away from the deposit source. To date, case studies using streams and groundwaters have demonstrated water to be an effective geochemical medium for vectoring sources of Cu, Au, diamonds, base metals (Pb and Zn), U, and Ni–platinum group elements (PGEs) mineralization. Significant technological advances in sample analysis (detection limits) and drilling methods (for groundwaters) have dramatically increased the detectable plume associated with mineral deposits and reduced the time and cost of acquiring groundwater samples. Hydrogeochemistry is an effective early stage greenfield and regional survey mineral exploration assessment tool. As explorers target ever deeper prospects or enter areas of post-mineral cover, hydrogeochemistry should be at the forefront of an explorer's geochemistry toolbox.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".