Groundwater‐Pond Interactions on a Remote Sand Island Driven by Oceanic and Meteorologic Processes
Bibliographic record
Abstract
ABSTRACT Islands are hotspots of cultural and biological diversity; however, small‐island populations and ecosystems face freshwater insecurity. Fresh surface water bodies on small islands are particularly limited in size and number and are less well studied than underlying freshwater lenses. Small islands with permeable geology often have a hydraulic connection among the ocean, groundwater system, and fresh surface water bodies, but these subsurface hydraulic linkages are poorly understood. Utilising in situ hydrologic and thermal monitoring and remote sensing, this study investigated how meteorologic (precipitation and evapotranspiration) and oceanic (waves, storm surge, and flooding) forcing drive pond level dynamics and groundwater–surface water interactions on a remote sand island (Sable Island) in the Northwest Atlantic. Stilling wells, piezometers, and sediment temperature rods were installed in five ponds from July 2020 to September 2021 and were collectively used to create area‐depth relationships, infer pond bed hydraulic conductivity, and calculate vertical groundwater fluxes. PlanetScope satellite images during this period were classified to quantify pond surface areas and provide a link to in situ monitoring to assess changes following meteorologic and oceanic events. Meteorological and coastal hydrodynamic data were used to determine drivers controlling pond levels and groundwater–surface water exchange rates. Findings reveal that pond levels and vertical exchange fluxes are influenced by tides, precipitation, waves, and beach flooding, but have the highest correlation to oceanic forcing (high significant wave heights) that drive beach flooding and groundwater level changes. Remote sensing paired with pond level–area relationships proved effective for monitoring surface water dynamics in this remote area. Results expand our understanding of small‐island hydrology and hydraulics and emphasise that surface water, groundwater, and the ocean are not separate but exist along a coupled hydrologic continuum. In a period of rapid environmental change, understanding fresh surface water dynamics on small islands is crucial to protecting vulnerable ecosystems from freshwater scarcity.
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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 | 0.000 |
| 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".