{"id":"W1965501796","doi":"10.1080/15567030903261832","title":"A Modified Differential Evolution Optimization Algorithm with Random Localization for Generation of Best-Guess Properties in History Matching","year":2011,"lang":"en","type":"article","venue":"Energy Sources Part A Recovery Utilization and Environmental Effects","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Differential evolution; Convergence (economics); Matching (statistics); Algorithm; Computer science; Simplex algorithm; Simplex; Meta-optimization; Mathematical optimization; Optimization problem; Mathematics; Linear programming","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":[0.00105681,0.0004409607,0.0006555295,0.0006355379,0.0003144014,0.0003860879,0.0009342199,0.0008488112,0.001875785],"category_scores_gemma":[0.002539502,0.0004089957,0.0003913786,0.0004968885,0.0005854138,0.000657674,0.0008225584,0.000432388,0.000289591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005663732,"about_ca_system_score_gemma":0.0007354502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002492691,"about_ca_topic_score_gemma":0.001996889,"domain_scores_codex":[0.9997228,0.0001007506,0.0000161358,0.00004690881,0.0000923977,0.0000210596],"domain_scores_gemma":[0.9993672,0.0003838389,0.00004977599,0.00005789135,0.0001188567,0.00002244478],"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":[0.0000755693,0.00004508006,0.0005871337,0.00004052752,0.00002436671,0.00005904018,0.00007200332,0.9000323,0.00689865,0.01157578,0.0004851544,0.08010436],"study_design_scores_gemma":[0.000005844684,0.000008530804,0.00002708541,0.000001263377,0.000001350251,0.000004263846,0.000001299487,0.9988452,0.0005483564,0.0003871281,0.0001674821,0.000002113109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01177198,0.0000343287,0.9872738,0.00003785132,0.00001032055,0.00003276346,0.000009051781,0.0001658717,0.0006640417],"genre_scores_gemma":[0.2979839,0.00004866524,0.6996978,0.00005747487,0.0000114142,0.0002811613,0.00006029769,0.0001072467,0.001752087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002492691,"threshold_uncertainty_score":0.006275117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03181733946876873,"score_gpt":0.1940908941978163,"score_spread":0.1622735547290476,"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."}}