New challenges in the study of coastal aquifers from numerical modeling. The case of the Motril-Salobreña aquifer
Bibliographic record
Abstract
Coastal aquifers are frequently complex systems with highly heterogeneous geological characteristics, specific flow patterns, presence of fluids with different densities, high risk of contamination both by salinization and by other pollutants, and highly modified by anthropic activity. Given this situation, numerical modeling becomes the most appropriate tool to determine the potential impact of global change on marine intrusion in this kind of aquifers. The mathematical models traditionally used in coastal aquifers have been those of flow and mass transport with variable density, which allow obtaining a distribution of salinities in the aquifer and reproducing the flow pattern in the area of discharge to the sea. In addition to these models, another type of numerical modeling can be applied that could also provide information on specific aspects of this type of aquifers. The study of the Motril-Salobreña coastal aquifer shows, for example, how heat transport models allow quantifying the recharge that occurs from rivers that frequently interact with coastal aquifers; as well as the age transport models provide data on the preferential groundwater circulation paths and its residence time, very relevant information in these frequently heterogeneous and anisotropic aquifers. Future challenges are directed towards determining the effects of sea level rise on marine intrusion and establishing the degree of equilibrium of the freshwater-saltwater contact with the current situation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".