{"id":"W2773185895","doi":"10.1016/j.marpolbul.2017.12.004","title":"Modeling marine oily wastewater treatment by a probabilistic agent-based approach","year":2017,"lang":"en","type":"article","venue":"Marine Pollution Bulletin","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador; Canada Foundation for Innovation","keywords":"Probabilistic logic; Biological system; Calibration; Naphthalene; Sensitivity (control systems); Computer science; Wastewater; Environmental science; Process (computing); Statistical model; Root mean square; Biochemical engineering; Process engineering; Chemistry; Environmental engineering; Mathematics; Statistics; Engineering; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001004214,0.001048151,0.001486804,0.0009171574,0.0007720326,0.001840573,0.002465687,0.00256651,0.004027297],"category_scores_gemma":[0.002512061,0.001319141,0.001473592,0.0008678689,0.001101699,0.001590151,0.001382636,0.001527086,0.0004041074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546058,"about_ca_system_score_gemma":0.001920843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05603886,"about_ca_topic_score_gemma":0.03267128,"domain_scores_codex":[0.9995449,0.0001467223,0.00002801217,0.00008899839,0.00009285885,0.00009847335],"domain_scores_gemma":[0.9984932,0.0009617971,0.0002148255,0.00003821106,0.0001855194,0.0001063533],"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":[0.000009789022,0.00001017436,0.0001829604,0.000006507978,0.00001164079,0.00001301472,0.000005472455,0.9984128,0.00005023844,0.0008393298,0.00003946323,0.0004185436],"study_design_scores_gemma":[0.000005443093,0.000004604829,0.00004327662,8.810321e-7,0.00000518834,0.000002318233,0.00000316741,0.9993644,0.00002050972,0.0004999117,0.00004834135,0.00000199353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3034581,0.0008548603,0.6716805,0.001675816,0.0002181362,0.0002160118,0.0009600191,0.0006581266,0.02027838],"genre_scores_gemma":[0.9656999,0.0003317654,0.02614219,0.0001072756,0.0000588213,0.0001604639,0.0002172027,0.0000453545,0.007237083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05603886,"threshold_uncertainty_score":0.1114253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473108759874431,"score_gpt":0.2135391211285984,"score_spread":0.198808033529854,"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."}}