Conceptual Framework for Groundwater Vulnerability Assessment Using Physical, Experimental and Machine Learning Based Approaches in Coastal Aquifers of India
Bibliographic record
Abstract
The exploitation of coastal aquifers results in intrusion of seawater and optimal management of coastal aquifers focuses mainly on adopting good operational strategies to contain aquifer salinity within a mandated limit, while simultaneously meeting the demand for water supply/recharge. Management of saltwater intrusion in coastal aquifers is thus a critical issue of modern times. In this study, we present a theoretical framework for assessing the groundwater vulnerability in coastal aquifers of India using physical, experimental, and machine learning-based approaches. The developed framework suggests the use of 2D experiment for understanding the saltwater intrusion processes and fate and transport of contaminants like fluoride and arsenic. Further, the obtained parameters from the 2D experiments will be used to develop a numerical model using a physical-based simulator (SEAWAT). Lastly, the physical-based simulator will be replaced by a machine learning-based model and later will be coupled with optimization approaches to solve the groundwater management problem in coastal aquifers. The suggested framework will be useful in developing the strategies for minimization of saltwater intrusion or maximization of freshwater pumping in coastal zones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".