MétaCan
Menu
Back to cohort
Record W1990875258 · doi:10.1155/2012/986867

A Fuzzy Simulation‐Based Optimization Approach for Groundwater Remediation Design at Contaminated Aquifers

2012· article· en· W1990875258 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMathematical Problems in Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsEnvironmental remediationGroundwater remediationFuzzy logicHazardous wasteGroundwaterAquiferEnvironmental scienceComputer scienceEnvironmental engineeringWaste managementEngineeringContaminationArtificial intelligenceGeotechnical engineering

Abstract

fetched live from OpenAlex

A fuzzy simulation‐based optimization approach (FSOA) is developed for identifying optimal design of a benzene‐contaminated groundwater remediation system under uncertainty. FSOA integrates remediation processes (i.e., biodegradation and pump‐and‐treat), fuzzy simulation, and fuzzy‐mean‐value‐based optimization technique into a general management framework. This approach offers the advantages of (1) considering an integrated remediation alternative, (2) handling simulation and optimization problems under uncertainty, and (3) providing a direct linkage between remediation strategies and remediation performance through proxy models. The results demonstrate that optimal remediation alternatives can be obtained to mitigate benzene concentration to satisfy environmental standards with a minimum system cost.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.790

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 head0.026
GPT teacher head0.210
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