Hydrodynamic Links between Shallow and Deep Mineralization Systems and Implications for Deep Mineral Exploration
Bibliographic record
Abstract
Abstract Deep mineral exploration is increasingly important for finding new mineral resources but there are many uncertainties. Understanding the factors controlling the localization of mineralization at depth can reduce the risk in deep mineral exploration. One of the relatively poorly constrained but important factors is the hydrodynamics of mineralization. This paper reviews the principles of hydrodynamics of mineralization, especially the nature of relationships between mineralization and structures, and their applications to various types of mineralization systems in the context of hydrodynamic linkage between shallow and deep parts of the systems. Three categories of mineralization systems were examined, i.e., magmatic‐hydrothermal systems, structurally controlled hydrothermal systems with uncertain fluid sources, and hydrothermal systems associated with sedimentary basins. The implications for deep mineral exploration, including potentials for new mineral resources at depth, favorable locations for mineralization, as well as uncertainties, are discussed.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".