Bibliographic record
Abstract
ABSTRACT Groundwater is an important medium for geochemical exploration of many different styles of mineralization, including porphyry copper, volcanogenic massive sulphide (VMS), sandstone uranium, and gold. Groundwater recharges to depth, resulting in greater likelihood of interacting with buried mineralization compared to surface geochemical methods, and thus providing a three-dimensional perspective. Advances in the understanding of ore formation processes, water-rock interaction and element transport/attenuation in the secondary environment are enhancing the efficacy of groundwater geochemical exploration. This paper describes key techniques and methodologies for sampling, analysis and interpretation of groundwater geochemical data, and provides two different approaches for use by industry: routine exploration and research approaches. New advances in analytical methods are providing new isotopic systems and improving the cost and speed of traditional isotopic techniques, which can greatly aid in interpretation of water sources, water-rock reactions and fingerprinting of ore sources. Case studies are presented for the use of groundwater geochemistry around a porphyry copper deposit in the hyperarid Atacama Desert of Chile, and VMS mineralization in a mature mining camp in Canada. This paper also summarizes key elemental associations for successful utilization of aqueous geochemistry in mineral exploration. The most successful aqueous-phase indicators of mineralization are those that are associated with the ore and are mobile in solution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".