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Record W2301782318 · doi:10.2166/wst.2003.0072

A GIS-based modeling system for petroleum waste management

2003· article· en· W2301782318 on OpenAlexaff
Z. Chen, Guohe Huang, Jianbing Li

Bibliographic record

VenueWater Science & Technology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsGeographic information systemEnvironmental remediationPetroleumComponent (thermodynamics)Environmental scienceContaminationGroundwater contaminationInformation systemGroundwaterComputer scienceEngineeringGeologyRemote sensing

Abstract

fetched live from OpenAlex

With an urgent need for effective management of petroleum-contaminated sites, a GIS-aided simulation (GISSIM) system is presented in this study. The GISSIM contains two components: an advanced 3D numerical model and a geographical information system (GIS), which are integrated within a general framework. The modeling component undertakes simulation for the fate of contaminants in subsurface unsaturated and saturated zones. The GIS component is used in three areas throughout the system development and implementation process: (i) managing spatial and non-spatial databases; (ii) linking inputs, model, and outputs; and (iii) providing an interface between the GISSIM and its users. The developed system is applied to a North American case study. Concentrations of benzene, toluene, and xylenes in groundwater under a petroleum-contaminated site are dynamically simulated. Reasonable outputs have been obtained and presented graphically. They provide quantitative and scientific bases for further assessment of site-contamination impacts and risks, as well as decisions on practical remediation actions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.007

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations16
Published2003
Admission routes1
Has abstractyes

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