Morphologic, Atmospheric, and Oceanic Drivers Cause Multi‐Temporal Saltwater Intrusion on a Remote, Sand Island
Bibliographic record
Abstract
Abstract Small‐island populations disproportionately rely on fresh groundwater resources, which are increasingly threatened by salinization from changing ocean and climate conditions. This study investigates island groundwater dynamics and salinization over multiple timescales in response to marine, atmospheric, and morphologic drivers. New geophysical and hydrological data sets were collected on a remote sand island in the Northwest Atlantic Ocean between 2020 and 2022 and compared to historical baseline data from the 1970s. Data reveal saltwater intrusion due to multi‐decadal erosion, seasonal climate patterns, tidal forcing, and episodic flooding from Atlantic hurricanes. Long‐term dune erosion has caused the freshwater lens to thin and become asymmetrical; however, a lagged groundwater response causes the freshwater lens to be in disequilibrium with present island morphology. The maximum vertical lens thickness is seasonally constant, but the lateral transition zone along the coast thickens and moves seaward in spring when the water table is high from precipitation and frequent beach flooding. Groundwater level and electrical conductivity along low‐lying beaches significantly increase following Atlantic hurricanes due to seawater flooding, and reach a maximum of 1.93 m above sea level and 38 mS/cm, respectively. While groundwater levels recover quickly, conductivity (salinity) remains elevated due to the short intervals between winter flood events that outpace freshening from meteoric recharge. Results emphasize the importance of multi‐temporal groundwater dynamics and feedbacks between coastal flooding, erosion, and salinization. Field‐based studies considering multiple drivers and timescales of saltwater intrusion are critical for understanding and managing coastal freshwater resources in an age of rapid environmental change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".