{"id":"W4394871298","doi":"10.1016/j.apenergy.2024.123179","title":"Physics-informed machine learning for noniterative optimization in geothermal energy recovery","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"King Abdullah University of Science and Technology","keywords":"Geothermal energy; Energy (signal processing); Geothermal gradient; Physics; Artificial intelligence; Computer science; Quantum mechanics","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":[0.00009353008,0.000156025,0.0001548191,0.0001454748,0.00003280704,0.00007326544,0.00006826223,0.00008902736,0.00003577808],"category_scores_gemma":[0.00001144441,0.0001601245,0.00005385041,0.0002820112,0.00001003704,0.0001548925,0.00001268498,0.0001114659,0.000002157957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008921846,"about_ca_system_score_gemma":0.00001747269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004845501,"about_ca_topic_score_gemma":0.00002296236,"domain_scores_codex":[0.9993404,0.00001359412,0.0001980784,0.0001558695,0.00008125813,0.0002107656],"domain_scores_gemma":[0.9996132,0.0002235069,0.00001377059,0.00009644759,0.00001440621,0.00003867445],"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":[0.00001412169,0.00000491163,0.000007195271,0.00005788965,0.00003200472,0.00000138803,0.0001492079,0.9637089,0.0004928023,0.01266294,0.00006067226,0.02280801],"study_design_scores_gemma":[0.0003031165,0.00001856385,0.00001006777,0.00002963493,0.000005382342,4.752191e-7,0.00001043007,0.9739113,0.003075806,0.0007360397,0.02172179,0.0001773865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004748772,0.0003063602,0.9851369,0.000006404597,0.00024127,0.00005336718,0.000005353391,0.00042791,0.009073672],"genre_scores_gemma":[0.962509,0.0002172549,0.03537168,0.00002517119,0.0002559478,0.0002064698,0.00023749,0.00008971553,0.001087289],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9577602,"threshold_uncertainty_score":0.6529688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031448399544061,"score_gpt":0.2399800996220253,"score_spread":0.2296656156265847,"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."}}