Investigation of offshore freshened groundwater using marine controlled–source electromagnetic: Insights from Gozo, Maltese Islands
Bibliographic record
Abstract
Carbonate margins form a significant portion of the world's coastlines, contain substantial freshwater resources, and play a vital role in global hydrogeological processes. These regions are promising reservoirs for offshore freshened groundwater (OFG), a potential resource for coastal and island populations. Mapping OFG within continental margins using marine controlled–source electromagnetic (CSEM) data relies on electrical resistivity as a proxy, requiring sophisticated inversion techniques. Given the ambiguity in deriving discrete resistivity distributions from CSEM data, understanding uncertainty is essential for reliable OFG inference. The conventionally used two–dimensional deterministic inversion provides a best–fit solution but does not assess resistivity uncertainties, limiting OFG characterization. To address this, we apply trans–dimensional Bayesian inversion on marine CSEM data from a semi–arid carbonate setting off eastern Gozo (Maltese Islands, Mediterranean Sea). Here, we integrate deterministic and trans–dimensional inversion results with seismic reflection data to identify two distinct, continuous resistivity anomalies within the Lower Coralline Limestone formation. The first, shallower resistive body starts ~4 km from the coast, appearing across all the CSEM profiles at 210–250 m below sea–level, with resistivity increasing landward. This anomaly may suggest an OFG body. The second, deeper anomaly starts at 350–400 m below sea–level and extends deeper. Whether it represents a second OFG unit or geological changes remains uncertain. Our findings offer new insights into resistivity distributions within carbonate margins, highlighting their OFG potential and the value of trans–dimensional sampling. This study augments CSEM research, underlining the need to extend coastal hydrogeological studies offshore for improved environmental conservation and resource management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".