Linking DPSIR Model and Water Quality Indices to Achieve Sustainable Development Goals in Groundwater Resources
Bibliographic record
Abstract
The achievement of sustainable development goals in groundwater resources related to water quality issues is a critical question in many regions. This study aims to combine powerful tools for helping stakeholders and policymakers achieve sustainable development goals in groundwater resources of agricultural areas. The DPSIR (Driver–Pressure–State–Impact–Response) model in combination with the Canadian Council of Ministers of Environment Water Quality Index and Groundwater Directive 2006/118/European Community—Threshold Values was applied using a hydrogeochemical dataset derived from the analysis of groundwater samples collected from 31 monitoring sites in an unconfined alluvial aquifer. Elevated Cl− (up to 423.2 mg L−1), NO3− (up to 180.1 mg L−1) concentration and electrical conductivity (up to 2037 μS cm−1) value are observed for groundwater samples of the study area. The outcome of the “One Out-All Out” procedure revealed that the groundwater in 42% of the monitored sites is unsuitable for drinking according to the health-based guideline values established by Directive 98/83/European Community. A difficulty to achieve targets under Sustainable Development Goals 3 and 6 in the study area is revealed. The proposed response actions are reported.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".