{"id":"W4383312623","doi":"10.1021/acs.energyfuels.3c01293","title":"Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs","year":2023,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of the Fraser Valley","funders":"Higher Education Commision, Pakistan; King Saud University","keywords":"Petrophysics; Support vector machine; Random forest; Computer science; Decision tree; Data mining; Artificial intelligence; Machine learning; Outlier; Boosting (machine learning); Algorithm; Geology; Geotechnical engineering; Porosity","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":[0.0003845655,0.0001701293,0.0004349125,0.0003913217,0.0001187323,0.00005598299,0.0002398256,0.00007451349,0.00001883133],"category_scores_gemma":[0.0002074546,0.0001759455,0.0001116826,0.0008931045,0.00003142834,0.0002211633,0.0001156605,0.0001301746,7.790309e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002067231,"about_ca_system_score_gemma":0.000009102992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002703925,"about_ca_topic_score_gemma":0.000006395161,"domain_scores_codex":[0.9988624,0.00007841583,0.0003174382,0.0003029768,0.000181779,0.0002570159],"domain_scores_gemma":[0.9988263,0.0005528241,0.00006172519,0.000424701,0.00005243753,0.00008202252],"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":[0.00001753381,0.000005110507,0.00005619482,0.00005348027,0.0004385842,0.00000389514,0.00008909884,0.9802691,0.01462913,0.0001421536,0.00002442763,0.004271271],"study_design_scores_gemma":[0.0005840703,0.00002364961,0.0002574199,0.00002488744,0.0001843302,9.141644e-7,0.0000378871,0.9853411,0.01136279,0.0001256537,0.001883256,0.0001739922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4676402,0.0005496306,0.5310082,0.00002318614,0.0001312197,0.00005458041,0.0001197339,0.0003982077,0.00007496878],"genre_scores_gemma":[0.8682122,0.0002857491,0.1295381,0.000006972221,0.00007723161,0.00003128608,0.001503419,0.00006182027,0.0002833001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4014702,"threshold_uncertainty_score":0.717485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03229532284741673,"score_gpt":0.3046049208089111,"score_spread":0.2723095979614943,"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."}}