Influence of instantaneous and time‐averaged groundwater flows induced by waves on the fate of contaminants in a beach aquifer
Bibliographic record
Abstract
Abstract Wave‐induced water exchange and groundwater flows in beach aquifers impact the fate of contaminants including nutrients, fecal bacteria, and nonaqueous phase liquids (NAPLs). Waves induce high‐frequency fluxes in shallow beach sediments. In addition, the phase‐averaged effect of waves (wave setup) drives deeper flow recirculations through a beach aquifer. Field data of shallow instantaneous and time‐averaged vertical head gradients (fluxes) are first compared with deeper time‐averaged fluxes over a period of varying wave conditions. The time‐averaged fluxes are equivalent to that which would be simulated assuming a phase‐averaged water surface (i.e., wave setup). Based on this comparison, the need to simulate phase‐resolved wave motion versus the simplified phase‐averaged water surface in predicting contaminant fate is evaluated. While high‐frequency fluxes cause large surface water volumes to filter through beach sediments, the exchanging water has a short residence time (<1–70 s). The time‐averaged flow behavior captures exchanging water with longer residence time (hours to months) and deeper flow paths. Therefore, consideration of the time‐averaged behavior may be sufficient for evaluating dissolved reactive constituents. In contrast, calculations indicate that instantaneous fluxes may need to be considered in evaluating colloidal contaminants (e.g., particulate organic matter and fecal bacteria) as sediment interactions affect their transport and residence time. Finally, multiphase simulations illustrate the differential effect of considering instantaneous versus time‐averaged fluxes on the downward migration of NAPL in beach sediments. This study provides an important foundation for future field and modeling efforts focused on understanding and predicting contaminant transport in wave‐influenced beaches.
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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".