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Record W2096467591 · doi:10.1080/009083190889951

NRSRM: A Decision Support System and Visualization Software for the Management of Petroleum-Contaminated Sites

2005· article· en· W2096467591 on OpenAlexaffabout
Xiaosheng Qin, Guohe Huang, Yuefei Huang, Guang Zeng, A. Chakma, Jianbing Li

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Northern British ColumbiaUniversity of WaterlooUniversity of Regina
Fundersnot available
KeywordsVisualizationRemedial educationDecision support systemComputer scienceVariety (cybernetics)Environmental remediationSoftwareContaminationRemedial actionRisk analysis (engineering)Systems engineeringEngineeringData miningArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

A robust decision-support system (DSS) was developed to provide environmental managers with an integrated measure for tackling subsurface contamination problems. Such a DSS included components of mathematical modeling, risk assessment, remediation-technique screening, and monitoring-program design. A visual-language-based software package, named NRSRM, was developed for facilitating a variety of functions within the DSS. It provided a collection of measures for analyzing and visualizing subsurface contamination problems. A contaminated site located in western Canada was examined to demonstrate its applicability. By exploring different remedial technologies, NRSRM recommended six alternatives for cleaning up the site. Users can click selected items under each alternative to acquire more information of the related actions and efficiencies.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.008

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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
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

Citations22
Published2005
Admission routes2
Has abstractyes

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Same venueEnergy Sources Part A Recovery Utilization and Environmental EffectsSame topicGroundwater flow and contamination studiesFrench-language works237,207