Regional, physics-based mapping of coastal seawater intrusion vulnerability
Bibliographic record
Abstract
Coastal communities are often reliant on fresh groundwater resources, but coastal aquifers are increasingly vulnerable to lateral saltwater intrusion (SWI) due to climate change impacts such as rising seas. Mapping SWI vulnerability to identify coastlines is challenging, particularly at large spatial scales given the limited subsurface data and associated difficulties with characterizing subsurface conditions. Empirical, parametric approaches are commonly applied for SWI vulnerability mapping at local to regional scales, as these methods rely on parameters that are easily extracted from geospatial datasets. Analytical solutions can more robustly quantify SWI vulnerability as they are grounded in physics and do not rely on arbitrary parameter weighting. This study advances established analytical methods, typically used in more local contexts, to map SWI vulnerability at much larger scales by using a new combined analytical-geospatial framework. We develop a novel ArcGIS toolbox to automate the extraction/calculation of hydrogeological parameters from public datasets to apply an analytical solution that maps SWI vulnerability along the 13,300 km coastline of Nova Scotia (NS), Canada. Northern NS will experience the highest SLR (1.1 m by year 2100), but the southern coast was identified as the ‘hot spot’ most vulnerable to SLR-induced SWI (e.g., 80 % of gridded points in Shelburne County). Overall, the results illustrate the utility of these approaches for first-order mapping of relative vulnerability. Outputs from such analyses can be used to prioritize areas for more detailed hydrogeological assessments and to communicate risks
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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".