Remediation Geology and Process-Based Conceptual Site Models to Optimize Groundwater Remediation
Bibliographic record
Abstract
The Environmental Consulting Industry in the United States has historically prioritized engineering approaches over geologic science in addressing groundwater contamination. This engineering-centric bias has often resulted in oversimplified conceptual site models (CSMs) that fail to capture subsurface heterogeneity, limiting the effectiveness of groundwater remediation strategies. Recognizing the critical role of geology, the industry is increasingly adopting a Remediation Geology approach, which emphasizes the development of robust geologic models as the foundation for remediation programs. Geologic models optimize site lithologic data to define subsurface permeability architecture. The geologic model primarily serves as the structure to develop a Process-Based CSM, which is a holistic model that supports the entire remediation life cycle. A Process-Based CSM addresses the physical, chemical, and biological processes governing contaminant occurrence with the goal of modeling and predicting subsurface conditions for improved decision making with respect to monitoring programs and remediation design. Case studies highlight the transformative impact of Remediation Geology and Process-Based CSMs, demonstrating significant improvements in cleanup efficiency and resource optimization across diverse hydrogeologic settings. By addressing site complexities such as fine-grained units and fracture networks, Remediation Geology and Process-Based CSMs have proven effective for contaminants ranging from chlorinated solvents to per- and polyfluoroalkyl substances (PFASs) and radionuclides.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".