{"id":"W2102808370","doi":"10.1504/ijep.2010.034236","title":"A fuzzy multi-criteria decision analysis approach for the management of petroleum-contaminated sites","year":2010,"lang":"en","type":"article","venue":"International Journal of Environment and Pollution","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"Australian Government","keywords":"Remedial education; Remedial action; Ranking (information retrieval); Fuzzy logic; Stakeholder; Multiple-criteria decision analysis; Environmental remediation; Site selection; Rank (graph theory); Selection (genetic algorithm); Contaminated land; Computer science; Fuzzy set; Analytic hierarchy process; Risk analysis (engineering); Operations research; Engineering; Mathematics; Contamination; Machine learning; Business; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005303422,0.001724924,0.001739474,0.00468714,0.001755246,0.003039923,0.002288975,0.001848943,0.003320387],"category_scores_gemma":[0.006113161,0.0006971389,0.002359452,0.002562405,0.001150258,0.001931268,0.001816987,0.002089686,0.0003931406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003146515,"about_ca_system_score_gemma":0.003891134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004536138,"about_ca_topic_score_gemma":0.007822596,"domain_scores_codex":[0.9950969,0.002431535,0.0002854064,0.000293719,0.001749941,0.0001424432],"domain_scores_gemma":[0.9979042,0.001289521,0.0001794323,0.00006729104,0.0004665399,0.00009309244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007438788,0.0002124812,0.0009092027,0.0009389705,0.0003934836,0.0003839683,0.0004727101,0.7024351,0.00499886,0.1030381,0.002302956,0.1838397],"study_design_scores_gemma":[0.00003206246,0.0001726933,0.0003173681,0.0001899827,0.00009508004,0.000157044,0.00017692,0.9275051,0.001310827,0.06241709,0.007550662,0.00007515556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002659037,0.0005361188,0.9933233,0.0002718679,0.00005046712,0.0002270493,0.00006337056,0.00005895007,0.002809821],"genre_scores_gemma":[0.1049273,0.000671545,0.8924115,0.0001256059,0.00005655734,0.0006385225,0.00008008225,0.00002189577,0.001067002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005303422,"threshold_uncertainty_score":0.02804756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0702390078454856,"score_gpt":0.3805419968063846,"score_spread":0.310302988960899,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}