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Record W2002317194 · doi:10.1002/rem.20067

Modeling remediation time using natural attenuation at a dry-cleaner site

2005· article· en· W2002317194 on OpenAlexaff
Sarah Hausman, Hanadi S. Rifai

Bibliographic record

VenueRemediation Journal · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsBiorem Technologies (Canada)
Fundersnot available
KeywordsEnvironmental remediationAttenuationEnvironmental scienceChlorinated solventsContaminationVinyl chlorideWaste managementNatural (archaeology)Environmental chemistryEnvironmental engineeringChemistryGeologyEngineeringEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.004

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.

Opus teacher head0.013
GPT teacher head0.229
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2005
Admission routes1
Has abstractyes

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