{"id":"W2911254811","doi":"10.1016/j.petrol.2019.02.001","title":"Application of fuzzy decision tree in EOR screening assessment","year":2019,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Enhanced oil recovery; Decision tree; Computer science; Data mining; Fuzzy logic; Ranking (information retrieval); Expert system; Tree (set theory); Petroleum engineering; Machine learning; Engineering; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002700393,0.0005511874,0.0008699641,0.002523607,0.0005393467,0.0009779545,0.000531306,0.0007342114,0.001568802],"category_scores_gemma":[0.004520011,0.0002210226,0.0007808045,0.001547216,0.0002069254,0.0009190948,0.0003980125,0.0004109475,0.0002202211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005092594,"about_ca_system_score_gemma":0.00101426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004013288,"about_ca_topic_score_gemma":0.003030832,"domain_scores_codex":[0.9984843,0.0007389163,0.0001051033,0.0001430216,0.0004318924,0.00009677713],"domain_scores_gemma":[0.9969867,0.002024178,0.0001351306,0.00007493728,0.0007012659,0.00007790981],"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":[0.000461709,0.0003256508,0.009640835,0.0004178256,0.0002138318,0.000283236,0.0001579331,0.5229018,0.01292595,0.009859035,0.001538149,0.4412741],"study_design_scores_gemma":[0.00001090634,0.0001264173,0.001186444,0.00002580093,0.00004878054,0.00006499753,0.00003031243,0.9922215,0.002421272,0.00323756,0.0006114079,0.00001453681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1125264,0.000782274,0.8810914,0.0001897206,0.00005243644,0.0001434687,0.0002564221,0.0003783187,0.004579713],"genre_scores_gemma":[0.7561079,0.0003817333,0.242253,0.00004878694,0.00002625883,0.00007161169,0.0001849875,0.00002048558,0.0009052825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004013288,"threshold_uncertainty_score":0.01428121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003903758223349576,"score_gpt":0.2338050711432515,"score_spread":0.2299013129199019,"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."}}