{"id":"W4409328243","doi":"10.1016/j.asoc.2025.113108","title":"Data-driven oil production strategy selection under uncertainties","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Shell Brasil; Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Computer Modelling Group","keywords":"Production (economics); Selection (genetic algorithm); Computer science; Oil production; Artificial intelligence; Petroleum engineering; Geology; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001789104,0.0007363703,0.00117204,0.0009415491,0.0003131166,0.001282227,0.001007104,0.001167337,0.00166779],"category_scores_gemma":[0.007728682,0.0007156064,0.0005202998,0.0007629443,0.0005668895,0.001192144,0.0009941837,0.0009357245,0.0002676423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009349434,"about_ca_system_score_gemma":0.001575519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005462551,"about_ca_topic_score_gemma":0.004119455,"domain_scores_codex":[0.9995357,0.0001388242,0.00002651204,0.00009493006,0.0001149572,0.00008903365],"domain_scores_gemma":[0.9944822,0.00434857,0.0002374262,0.0001489228,0.0005946781,0.0001882906],"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":[0.0001067614,0.00003342477,0.0006738364,0.00002655874,0.00001584208,0.00004405225,0.0000141096,0.9873888,0.0004632377,0.0009157134,0.0002063751,0.01011119],"study_design_scores_gemma":[0.000003854234,0.000007856462,0.00004954425,0.000001373523,0.000001715162,0.00000286975,0.000002548443,0.9992137,0.0001959311,0.0004926725,0.0000267154,0.000001273655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3403105,0.0003931677,0.6526185,0.000819742,0.00008292393,0.0001434216,0.0005921648,0.0007054738,0.004334026],"genre_scores_gemma":[0.9758361,0.00004464116,0.02290649,0.00005971751,0.00001426665,0.00005841827,0.000251276,0.00003182562,0.0007971946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005462551,"threshold_uncertainty_score":0.01086152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03226754930495712,"score_gpt":0.2946437274732819,"score_spread":0.2623761781683248,"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."}}