Hydrochemical Characteristics and Evolution of Underground Brine During Mining Process in Luobei Mining Area of Lop Nur, Northwestern China
Bibliographic record
Abstract
Underground brine as a liquid mineral resource available for development and utilization has attracted widespread attention. However, how the mining process affects the hydrochemical characteristics and evolution of underground brine has yet to be fully understood. Herein, 207 underground brine samples were collected from the Luobei mining area of the Lop Nur region during pre-exploitation (2006), exploitation (2019), and late exploitation (2023) to explore the dynamic change characteristics and evolution mechanisms of the underground brine hydrochemistry using the combination of statistical analysis, spatial interpolation, correlation analysis, and ion ratio analysis. The results indicated that Na+ and Cl− were the dominant ionic components in the brine, and their concentrations remained relatively stable throughout the mining process. However, the content of Mg2+ increased gradually during the mining process (increased by 45.08% in the middle stage and 3.09% in the later stage). The elevation in Mg2+ concentration during the mining process could be attributed to the dissolution of Mg-bearing minerals, reverse cation exchange, and mixed recharge. This research furnishes a scientific foundation for a more in-depth comprehension of the disturbance mechanism of brine-mining activities on the groundwater chemical system in the mining area and for the sustainable exploitation of brine resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".