{"id":"W2170336234","doi":"10.1002/cjce.21802","title":"A developed smart technique to predict minimum miscible pressure—eor implications","year":2013,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Petroleum engineering; Particle swarm optimization; Miscibility; Artificial neural network; Enhanced oil recovery; Engineering; Computer science; Mathematics; Chemistry; Mathematical optimization; Machine learning; Polymer","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002064294,0.0001685887,0.000205138,0.0002217489,0.00004349903,0.00007894399,0.0005365862,0.0001172067,0.0001021752],"category_scores_gemma":[0.0002307274,0.0001416682,0.00007612338,0.0003287004,0.00002882517,0.0002127529,0.00002042164,0.0004183211,0.00002678538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000233908,"about_ca_system_score_gemma":0.0001762421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003458592,"about_ca_topic_score_gemma":0.0001227812,"domain_scores_codex":[0.9990224,0.000007908066,0.0003628948,0.00009394302,0.0001249169,0.0003879295],"domain_scores_gemma":[0.9988851,0.00008467984,0.00004322953,0.0002581583,0.0001597231,0.0005691175],"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":[0.000002012404,0.000004028255,0.00009332393,0.00006095347,0.0000864726,0.000004800138,0.0001621664,0.02689487,0.9502822,0.000261303,0.02038543,0.001762463],"study_design_scores_gemma":[0.000118051,0.00003338997,0.0006991595,0.0002243076,0.00003544043,0.0001954359,0.000005133029,0.006062744,0.9600736,0.0009483416,0.03128269,0.0003217029],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7787124,0.001302402,0.2041938,0.004595696,0.0008469025,0.001896159,0.00006213057,0.0007544513,0.007636054],"genre_scores_gemma":[0.9782466,0.000004343166,0.021238,0.00008755827,0.0001228861,0.0001493093,0.000001463882,0.00005005363,0.00009975432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1995342,"threshold_uncertainty_score":0.577706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007604620813164978,"score_gpt":0.1970724372458656,"score_spread":0.1894678164327006,"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."}}