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Integrated Subsurface Modeling and Risk Assessment of Petroleum-Contaminated Sites in Western Canada

2003· article· en· W2066722350 on OpenAlexaffabout
Zhenhu Chen, Guohe Huang

Bibliographic record

VenueJournal of Environmental Engineering · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsEnvironmental remediationEnvironmental planningVariety (cybernetics)Environmental sciencePetroleumIdentification (biology)Risk assessmentRisk managementGroundwaterRisk analysis (engineering)Environmental resource managementEnvironmental engineeringEngineeringContaminationComputer scienceBusiness

Abstract

fetched live from OpenAlex

Soil and groundwater contamination can pose a variety of impacts and risks to communities. Identification of management schemes with sound environmental and socio-economic efficiencies is desired. Before any decisions regarding site remediation actions can be made, three major questions may have to be answered. They are namely “What happened underground?”, “What will happen in the future under the given remediation scenarios?”, and “Are there specific risks to the surrounding community?”. In this study, an integrated modeling and risk assessment method is developed for effectively managing petroleum-contaminated sites through technically answering the above questions. It presents an integral concept that integrates issues of multicontaminant transport simulation, biodegradation modeling, health risk assessment, and site remediation for real-world problems within a general decision support framework. The developed method is applied to a petroleum-contaminated groundwater system in western Canada for identifying cost-effective management schemes with improved environmental and socio-economic efficiencies. The research outputs are directly useful for the decision maker to gain insight into the site and to make remediation decisions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.005
GPT teacher head0.182
Teacher spread0.177 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2003
Admission routes2
Has abstractyes

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