Recent advances in the application of mineral chemistry to exploration for porphyry copper–gold–molybdenum deposits: detecting the geochemical fingerprints and footprints of hypogene mineralization and alteration
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Bibliographic record
Abstract
In the past decade, significant research efforts have been devoted to mineral chemistry studies to assist porphyry exploration. These activities can be divided into two major fields of research: (1) porphyry indicator minerals (PIMs), which are used to identify the presence of, or potential for, porphyry-style mineralization based on the chemistry of magmatic minerals such as zircon, plagioclase and apatite, or resistate hydrothermal minerals such as magnetite; and (2) porphyry vectoring and fertility tools (PVFTs), which use the chemical compositions of hydrothermal minerals such as epidote, chlorite and alunite to predict the likely direction and distance to mineralized centres, and the potential metal endowment of a mineral district. This new generation of exploration tools has been enabled by advances in and increased access to laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), short-wave length infrared (SWIR), visible near-infrared (VNIR) and hyperspectral technologies. PIMs and PVFTs show considerable promise for exploration and are starting to be applied to the diversity of environments that host porphyry and epithermal deposits globally. Industry has consistently supported development of these tools, and in the case of PVFTs encouraged by several successful blind tests where deposit centres have successfully been predicted from distal propylitic settings. Industry adoption is steadily increasing but is restrained by a lack of the necessary analytical equipment and expertise in commercial laboratories, and also by the ongoing reliance on well-established geochemical exploration techniques (e.g. sediment, soil and rock chip sampling) that have aided the discovery of near-surface resources over many decades, but are now proving less effective in the search for deeply buried mineral resources and for those concealed under cover. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17
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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) | 0.000 | 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 it