Effects of Geologic Setting on Contaminant Transport in Deltaic Aquifers
Bibliographic record
Abstract
Abstract Coastal deltaic aquifers are vulnerable to degradation from seawater intrusion, geogenic and anthropogenic contamination, and groundwater abstraction. The distribution and transport of contaminants are highly dependent on the subsurface sedimentary architecture, such as the presence of channelized features that preferentially conduct flow. Surface deposition changes in response to sea‐level rise (SLR) and sediment supply, but it remains unclear how these surface changes affect the distribution and transport of groundwater solutes in aquifers. Here, we explore the influence of SLR and sediment supply on aquifer heterogeneity and resulting effects on contaminant transport. We use realizations of subsurface heterogeneity generated by a process‐based numerical model, DeltaRCM, which simulates the evolution of a deltaic aquifer with different input sand fractions and rates of SLR. We simulate groundwater flow and solute transport through these deposits in three contamination scenarios: (a) vertical transport from widespread contamination at the land surface, (b) vertical transport from river water infiltration, and (c) lateral seawater intrusion. The simulations show that the vulnerability of deltaic aquifers to seawater intrusion correlates to sand fraction, while vertical transport of contaminants, such as widespread shallow contamination and river water infiltration, is influenced by channel stacking patterns. This analysis provides new insights into the connection between the depositional system properties and vulnerability to different modes of groundwater contamination. It also illustrates how vulnerability may vary locally within a delta due to depositional differences. Results suggest that groundwater management strategies may be improved by considering surface features, location within the delta, and the external forcings during aquifer deposition.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".