{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001488268,0.00006980007,0.0001788585,0.00009483823,0.00003270319,0.000009336238,0.0001840451,0.00006531514,0.0000374196],"category_scores_gemma":[0.0002196413,0.00005396033,0.000102733,0.0003797841,0.00004910664,0.00003614817,0.000007008427,0.0001341025,6.586426e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007034666,"about_ca_system_score_gemma":0.0001058731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002037509,"about_ca_topic_score_gemma":0.00003161679,"domain_scores_codex":[0.9993217,0.000002321981,0.000326876,0.00005497317,0.0001160411,0.0001780722],"domain_scores_gemma":[0.9994527,0.00007214205,0.0001528503,0.0001004711,0.000119553,0.0001023069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000269742,0.000004513486,0.0002002994,0.0001401787,0.0000661349,6.066556e-7,0.00007371094,0.0498627,0.9479924,0.000105843,0.0004940825,0.001032508],"study_design_scores_gemma":[0.0001069827,0.00001475502,0.00005978543,0.00004181588,0.00008988326,0.000008393718,0.00002766404,0.07214831,0.9269297,0.00009529286,0.0004306484,0.00004682442],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983713,0.0004204411,0.0007631156,0.0002129474,0.00006782266,0.0000226783,0.0000343981,0.00001464816,0.00009265193],"genre_scores_gemma":[0.9992791,0.000003345571,0.0001688396,0.000005566055,0.0004293867,0.000006048644,0.000009324956,0.00001253325,0.00008581574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0222856,"threshold_uncertainty_score":0.2200438,"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."}}