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Mathematical, Physical, and Chemical Interpretations of Structural Control and Contributions to Gold Mineralization

2025· preprint· en· W4406408991 on OpenAlexaboutno aff
Jianzhao Yin, K. Y. Li, Hongyun Shi, Haoyu Yin, Yuhong Chao

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsMineralization (soil science)ChemistryEarth scienceEnvironmental scienceGeologySoil science

Abstract

fetched live from OpenAlex

Using the gold provinces in the northern margin of the North China Platform and the western Canadian orogenic belt—separated by thousands of miles—as examples, this article examines the temporal and spatial relationship between global gold deposits and geological structures; that is, gold metallogenic series within a region often correspond to the fault structural systems in that area. Fault structures at different hierarchical levels within the same fault system play distinct roles in gold deposit formation: the first-level deep and large faults are often the channels for the rise of rock-forming materials and their carriers, and control countless gold belts and/or gold fields on the earth's surface. The secondary fault system controls gold fields and/or gold deposits of different sizes. The third-level fault controls the gold ore body of a specific deposit. The mechanical properties of faults, their activity periods, and the development of structural rocks influence the timing and stages of mineralization, as well as the intensity and structure/texture of the resulting ore deposits. Post-ore-forming structures play a crucial role in converting primary gold deposits into placer gold deposits through processes such as uplift, displacement, transformation, and destruction of the original deposits. The deep-seated mechanisms underlying the structural control of gold mineralization align with principles of mathematics, physics, and chemistry. Nonlinear coherent effects within the far-from-equilibrium dissipative structures of fold-fault-ore-forming systems are significant. The localized dissipative structures resulting from tectonic and/or structural activity drive the final stages of gold mineralization. The essence of mineralization is the result of steady-state instability developing into a dissipative structure far away from equilibrium. The structures facilitate the conduction, extraction, further enrichment, and upward intrusion of deep ore-bearing fluids through special physical effects like seismic pumping, expansion, and re-fracture of mineralized zones. Deep and large faults also contribute to the formation of gold belts, ore fields, and deposits by inducing localized melting of rocks at different depths and mixing within the same tectonic system, as well as certain mutations in the ore-forming fluid entering the nonlinear low-pressure expansion zone, which causes hydrothermal boiling and a rapid nonlinear decrease in the solubility of the ore fluid. From a chemical perspective, the low-pressure expansion spaces formed by these structures act as vast mineralization reactors, in which rock-forming and mineralizing materials from different sources undergo violent physical and chemical reactions. This melting reactor not only greatly changes the temperature and pressure of the fluid, but also leads to drastic changes in many chemical conditions such as oxygen fugacity, pH, and electrolyte balance, thus creating conditions for the formation of gold deposits. Some structures, such as epigenetic faults, intersect and divide each other to create differences in the bottom topography, hydrodynamics, hydrogeochemistry, and physical chemistry in the basin. These variations create favorable space for the accumulation of large quantities of minerals, contributing to the formation of large or even super-large gold deposits.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.337
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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