Effects of Initial Solute Distribution on Contaminant Availability, Desorption Modeling, and Subsurface Remediation
Bibliographic record
Abstract
Low permeability regions in which solute movement is governed by diffusion reduce the availability of pollutants for remediation and can function as long-term sources of groundwater contamination. The inherent difficulty in understanding mass transfer from these regions of sequestered contamination is further complicated by unknown solute distributions within the low-permeability regions (sequestering regions). When models are calibrated to reproduce temporal histories of solute release from a sequestering region (desorption), the fitted parameter values are used to infer the physical or chemical characteristics of the media; however, the calibrated parameters also reflect the case-specific initial conditions (i.e., the solute distribution within the sequestering region domain at the onset of desorption). This phenomenon is demonstrated using model simulations of solute diffusion from hypothetical solids with characteristics similar to those of the well studied Borden, Ontario aquifer system. Solute release from the solids is simulated using a batch diffusion model under different initial solute distributions within the solids. The results of these model simulations are used to calibrate parameters of a multiple first-order rate desorption model (MRM) to illustrate how the fitted MRM parameters increase or decrease depending on the initial "aging" of the solids. Further numerical simulations are conducted for a one-dimensional flow system under steady-state and variable-rate hydraulic flushing. These simulations show that although aging reduces desorptive mass flux during early stages of flushing, aged sites have greater desorptive mass flux (greater solute availability) than "freshly" contaminated media during the later stages of remediation. Overall, the results demonstrate why the physicochemical meaning of observed desorption rates cannot be accurately deduced without first understanding the initial solute distribution within the media.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".