Assessment of seawater intrusion in coastal aquifers by modified CCME-WQI Indicators: Decadal dynamics in North Jiaozhou Bay, China
Bibliographic record
Abstract
Seawater intrusion (SWI) poses a growing threat to groundwater sustainability in the northern coastal region of Jiaozhou Bay (NCRJB), China. Quantifying SWI impacts is critical for developing targeted groundwater management strategies. This study proposes an enhanced version of the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), which integrates eight hydrogeochemical indicators to evaluate SWI dynamics in both porous and fractured aquifers in NCRJB. Statistical analysis of 131 groundwater samples collected during 2010–2011 and 2020 demonstrated pronounced salinization in porous aquifers, with 90.11% of samples classified as exhibiting severe SWI impacts. Fractured aquifers exhibited increasing intrusion severity, with the proportion of samples indicating significant intrusion rising to 35.29% by 2020. The modified CCME-WQI outperformed conventional single-indicator assessment methods based on chloride concentrations by detecting nuanced ion-exchange mechanisms and freshening processes in aquifer systems. SWI in NCRJB is driven by the interplay of natural climatic variability and anthropogenic activities. Our results demonstrate the framework’s enhanced sensitivity to heterogeneous aquifers and its potential as a transferable tool for SWI assessment in coastal regions worldwide. This research highlights the urgency of implementing adaptive coastal groundwater management strategies while providing a scientifically robust methodology for global SWI monitoring and mitigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".