Seawater intrusions for coastal aquifers are increasingly dominated by anthropogenic drivers in the Southern Mediterranean Basin
Bibliographic record
Abstract
Study Region Coastal aquifers at river outlets in North Africa are facing rapid degradation due to increasing aridity. Of particular interest is how these degradations extend to vulnerable lagoonal systems in semi-arid areas that are suffering from recent, accentuated climate fluctuations. Among these, the Medjerda River and Ghar El Melh Lagoon exhibit notable hydroclimate complexity, which is representative of several lagoonal systems in the southern Mediterranean Basin that remain poorly quantified. Study Focus To address this deficiency, we utilize GALDIT-AHP statistical methods to assess the susceptibility of coastal aquifers to seawater intrusion under decadal hydroclimatic fluctuations from 1985 to 2023. GALDIT combines aquifer hydraulic conductivity, groundwater levels, proximity to the coastline, seawater intrusion, and aquifer thickness with piezometric data and historical records spanning 1997–2022, encompassing hydrogeological, hydrological, and geospatial parameters during wet and dry seasons. New Hydrological Insights for the Region Our results indicate that aquifer vulnerability in these lagoonal systems is primarily governed by anthropogenic extraction, rather than the river basin’s seasonal hydraulic conditions. The correlation between GALDIT-AHP vulnerability index and seawater intrusion is well established. Vulnerability is most pronounced during the dry season, with hotspots concentrated along lagoon coastlines and in highly urbanized areas. Our findings underscore the need to reinforce sustainable groundwater management practices in the lagoonal systems of the southern Mediterranean Basin, thereby mitigating the growing and potentially irreversible seawater intrusion associated with increasing hydroclimatic fluctuations in coastal watersheds.
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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 | 0.001 |
| 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) | 0.001 | 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".