{"id":"W1979763803","doi":"10.1021/es071106y","title":"Optimization of Surfactant-Enhanced Aquifer Remediation for a Laboratory BTEX System under Parameter Uncertainty","year":2008,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of Waterloo","funders":"","keywords":"Environmental remediation; Parametric statistics; Aquifer; BTEX; Simulated annealing; Groundwater remediation; Nonlinear system; Mathematical optimization; Inference; Reliability (semiconductor); Computer science; Environmental science; Reliability engineering; Petroleum engineering; Engineering; Mathematics; Groundwater; Statistics; Benzene; Chemistry; Geotechnical engineering; Contamination","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0005610698,0.0004775742,0.0006796683,0.0002274923,0.0002901993,0.0006582095,0.0004476855,0.0007360363,0.0008156399],"category_scores_gemma":[0.001148735,0.0003306715,0.0004331508,0.0002639959,0.0005184766,0.0005006737,0.000396555,0.0004443958,0.00006981556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134825,"about_ca_system_score_gemma":0.001606434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01202831,"about_ca_topic_score_gemma":0.007729553,"domain_scores_codex":[0.9998063,0.0000647647,0.000008344277,0.00004495319,0.00003831528,0.00003732114],"domain_scores_gemma":[0.9995454,0.00029633,0.0000736245,0.00001883329,0.00004722346,0.00001853793],"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":[0.00002587058,0.00001510139,0.0002328239,0.00001187503,0.000004379062,0.00001873891,0.000006802233,0.9959746,0.001952495,0.0004866028,0.00002659849,0.001244152],"study_design_scores_gemma":[0.0000063483,0.00003349416,0.00009801267,8.015514e-7,0.000002815633,0.000003343603,0.000004874833,0.9982256,0.001331859,0.0002409035,0.00004952821,0.000002427473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7087646,0.0002561469,0.285794,0.0003910977,0.00001226853,0.0001333803,0.00023044,0.0002350343,0.004182959],"genre_scores_gemma":[0.9837462,0.00008749885,0.01490203,0.00001479159,0.000002091306,0.00008070779,0.00005102809,0.000013121,0.001102601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01202831,"threshold_uncertainty_score":0.0239166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008915795075236863,"score_gpt":0.2071537295445799,"score_spread":0.1982379344693431,"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."}}