Using hydrochemical, stable isotope, and river water recharge data to identify groundwater flow paths in a deeply buried karst system
Bibliographic record
Abstract
Abstract The deeply buried river‐connected Xishan karst aquifer (XKA) in western Beijing, China, has been suffering from diminishing recharge for several decades, which in turn leads to the disappearing of spring water outflows and continuously lowering of groundwater level in the area. Thus, it is important to correctly recognize the groundwater recharge and flow paths for the sustainable development of the XKA. To investigate these issues, the hydrochemical and isotopic compositions are analysed for both surface water and groundwater samples collected over an area of about 280 km2. Results show that (a) the river water is characterized by high Na contents; (b) the δ2H and δ18O values in the river water are distinctively higher than those of groundwater samples, after experiencing the long‐time evaporative enrichment in the upstream reservoir; (c) the Sr concentrations and 87Sr/86Sr ratios of groundwater clearly indicated the interaction between water and carbonate minerals but excluded the water–silicate interaction; and (d) the groundwater samples in the direct recharge area of the XKA have the lowest Na concentrations and the δ2H and δ18O values. Based on the large differences in the Na contents and 18O values of groundwater and surface water, a simple two‐component mixing model is developed for the study area and the fractions of the river water are estimated for groundwater samples. We find that the distribution pattern of the river water fractions in the XKA clearly shows a change of directions in the preferential flow path of the groundwater from its source zone to the discharge area. Overall, our results suggest that the recharged surface water can be a useful evidence for delineating the groundwater flow path in river‐connected karst aquifer. This study improves our understanding of the heterogeneity in karst groundwater systems.
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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".