{"id":"W4313334050","doi":"10.1016/j.fuel.2022.127194","title":"Prediction of minimum miscibility pressure (MMP) of the crude oil-CO2 systems within a unified and consistent machine learning framework","year":2022,"lang":"en","type":"article","venue":"Fuel","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Miscibility; Mean squared error; Mean absolute percentage error; Artificial neural network; Mean absolute error; Matrix (chemical analysis); Convolutional neural network; Mathematics; Applied mathematics; Computer science; Statistics; Artificial intelligence; Biological system; Chemistry; Chromatography; Polymer","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003888814,0.00009663311,0.0002442816,0.00003011327,0.000188071,0.00001779049,0.0001720268,0.00007222541,0.0001062199],"category_scores_gemma":[0.0002588544,0.00007404953,0.00008702357,0.0001702753,0.0001092029,0.00002476353,0.0001417063,0.0004215826,1.866345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002343832,"about_ca_system_score_gemma":0.00005399345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002516683,"about_ca_topic_score_gemma":0.000008091368,"domain_scores_codex":[0.9988933,0.0001186359,0.0003312346,0.0002295099,0.000323033,0.000104284],"domain_scores_gemma":[0.9990958,0.0001087149,0.000364968,0.0003327904,0.00006234012,0.00003537708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004216572,0.0006402232,0.4724061,0.01117693,0.001166362,0.000006275947,0.008622878,0.04987691,0.4534184,0.0004863347,0.0002442521,0.001533616],"study_design_scores_gemma":[0.006069492,0.0005712349,0.02559805,0.004226869,0.005288118,0.0002023176,0.03831646,0.7608759,0.09247237,0.002095336,0.06286015,0.001423751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897999,0.005500641,0.00003090385,0.0001042849,0.0001073869,0.00001093794,0.0002242359,0.00003563209,0.004186045],"genre_scores_gemma":[0.9886271,0.00002149712,0.00006057043,0.000009335441,0.00003256221,0.00001481352,0.00001859793,0.000009473471,0.01120602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.710999,"threshold_uncertainty_score":0.3019652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619079415702815,"score_gpt":0.2180847203591975,"score_spread":0.2018939262021694,"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."}}