Finite element modeling of pore-fluid flow in the Dachang ore district, Guangxi, China: Implications for hydrothermal mineralization
Bibliographic record
Abstract
Convective heat transfer associated with the circulation of pore-fluid in porous rocks and fractures within the upper crust of the Earth is substantial when the temperature gradient is sufficiently high. In order to understand the process of Sn-polymetallic mineralization in the Dachang ore district of Guangxi, a finite element method has been used in this study to simulate both pore-fluid flow and heat transfer in this district. On the basis of related geological, tectonic and geophysical constraints, a computational model was established. It enables a computational simulation and sensitivity analysis to be carried out for investigating ore-forming pore-fluid flow and other key factors that may affect hydrothermal ore genesis in the district. The related simulation results have indicated that: (1) permeable fault zones in the Dachang ore district can serve as preferential pathways for pore-fluid flow on a regional-scale; and (2) the pore-fluid flow can affect the salinity distribution. This latter factor is part of the reason why Sn-polymetallic mineralization has taken place in this district.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".