Modeling remediation time using natural attenuation at a dry-cleaner site
Bibliographic record
Abstract
Contaminants from dry-cleaning sites, primarily tetrachloroethene (PCE), trichloroethene (TCE), cis-dichloroethene (cis-DCE), and vinyl chloride (VC), have become a major concern because of the limited funds and regulatory programs to address them. Thus, natural attenuation and its effectiveness for these sites needs to be evaluated as it might provide a less costly alternative to other remediation methods. In this research, data from a site in Texas were analyzed and modeled using the Biochlor analytical model to evaluate remediation times using natural attenuation. It was determined that while biodegradation and source decay were occurring at the site, the resulting attenuation rates were not adequate to achieve cleanup in a reasonable time frame without additional source remediation or control strategies. Cleanup times exceeded 100 years for all constituents at the site boundary and 800 years at the source for PCE, assuming cleanup levels of 0.005 mg/L for PCE and TCE and 0.07 mg/L and 0.002 mg/L for cis-DCE and VC, respectively. © 2005 Wiley Periodicals, Inc.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".