{"id":"W4378084446","doi":"10.1002/cjce.24938","title":"Application of artificial neural network for prediction of 10 crude oil properties","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"API gravity; Artificial neural network; Mean squared error; Light crude oil; Mean absolute percentage error; Distillation; Oil refinery; Environmental science; Fourier transform infrared spectroscopy; Petroleum industry; Petroleum; Mathematics; Crude oil; Biological system; Petroleum engineering; Statistics; Computer science; Chemistry; Engineering; Artificial intelligence; Waste management; Chromatography; Environmental engineering; Chemical engineering; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001060918,0.001258467,0.000493761,0.001249471,0.0002749961,0.0006393026,0.0004957476,0.0007113554,0.0008682076],"category_scores_gemma":[0.002918033,0.0002863325,0.0005844282,0.0007309839,0.0001603976,0.0005511091,0.0003278186,0.0006428328,0.0002407918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009902541,"about_ca_system_score_gemma":0.0008650087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03006828,"about_ca_topic_score_gemma":0.01706149,"domain_scores_codex":[0.9996715,0.00008185882,0.00002972966,0.00008161188,0.00009457226,0.00004076519],"domain_scores_gemma":[0.9989339,0.0006012138,0.00009784603,0.00004257059,0.0002981278,0.00002630915],"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":[0.0001967219,0.0002067669,0.01908118,0.00007718919,0.0001230951,0.000104598,0.00002806693,0.9132868,0.003543519,0.0001923181,0.0006048505,0.06255496],"study_design_scores_gemma":[0.00000196829,0.0000143742,0.001397735,0.000003927855,0.000005081879,0.000003721242,0.000005410128,0.9976562,0.0007904209,0.00006317596,0.00005454841,0.000003412342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8274127,0.001151181,0.1647537,0.0002978028,0.0001233511,0.000105257,0.001008216,0.001422728,0.003725047],"genre_scores_gemma":[0.980289,0.0001489927,0.01813024,0.00002629178,0.000008826639,0.00004199837,0.0005142951,0.00001677281,0.0008235418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03006828,"threshold_uncertainty_score":0.0597865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980980440734992,"score_gpt":0.2159569766223331,"score_spread":0.1961471722149832,"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."}}