Simple Modeling Tool for Reconstructing Source History Using High Resolution Contaminant Profiles From Low‐k Zones
Bibliographic record
Abstract
An innovative but simple analytical modeling tool for reconstructing contaminant concentration versus time trends (i.e., “source history”) for a site using high‐resolution contaminant profiles from low permeability (low‐k) zones was developed and tested. Migration of contaminants into low‐k zones via diffusion (and possibly slow advection) produce concentration versus depth profiles that can be used to understand temporal concentration trends at the interface with overlying transmissive zones, including evidence of attenuation over time due to source decay. A simple transport‐based spreadsheet tool for generating source history estimates fit to the profiles was developed and applied to published soil concentration versus depth data from five distinct areas of four different sites contaminated with chlorinated ethenes. Using the root mean square error as an optimization metric, strong fits between measured and model‐predicted soil data were obtained in the majority of cases using site‐specific values for input parameters. In general, significant improvements could not be obtained by varying these parameters. As a result, the source history estimates generated by the tool were similar to those that had already been generated using more intensive analytical or numerical inverse modeling approaches. This included confirmation of constant source histories at locations where dense nonaqueous‐phase liquid was present (or suspected to be present), and declining source histories for locations where source isolation and/or attenuation had occurred. The advantage of the modeling tool described here is that it provides a simpler yet more dynamic method for understanding source behavior over time than existing approaches. ©2015 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".