Study on the coupling mechanism of karst groundwater flow path to the change of hydrochemical composition in earthquake region
Bibliographic record
Abstract
Water resource is a high degree of unity between quantity and quality, once the water body suffers from pollution will make the water resources more scarce, and karst groundwater resources is one of the main water resources in the seismic area.In this paper, we chose Baiquanquan area in the lowmountain hilly area at the eastern foot of the south section of Taihang Mountains in H province as the research object, set 25 sampling points and collected 20 groups of karst groundwater samples and 5 groups of surface water samples, and carried out the reliability test by the ion balance method to control the error within ±5%.Based on the karst groundwater samples, the general characteristics of its hydrochemistry were analysed, and its hydrochemical characteristics were explored by cluster analysis.The causes of hydrochemical ions in karst groundwater were investigated by Gibbs plot, chlor-alkali index and saturation index, and the related factors affecting the hydrochemical characteristics of karst groundwater were investigated by factor analysis.The hydrochemical cations and anions in karst groundwater were mainly composed of Ca 2+ and 3 HCO , and the average concentrations of the two were 132.15 mg/L and 193.66 mg/L, respectively.The cast points of karst groundwater all fell between the dolomite and calcite areas, and their Mg 2+ /Ca 2+ values ranged from 0.11 to 0.75, and the contribution of the F1 factor composed of Ca 2+ , Mg 2+ , 2 4 SO , TDS, 3 HCO was the maximum of 38.91%.Karst groundwater in the seismic area will be affected by rock weathering, human activities, etc., which will affect the flow path of karst groundwater, and then have an impact on the hydrochemical composition of karst water.
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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 | 0.001 |
| 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) | 0.001 | 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".