{"id":"W4376503915","doi":"10.1080/19648189.2023.2212029","title":"Development of linear-element boundary element method for inverse solution from induced far-field displacements to reservoir loading source","year":2023,"lang":"en","type":"article","venue":"European Journal of Environmental and Civil engineering","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Rock and Soil Mechanics, Chinese Academy of Sciences; State Key Laboratory of Geomechanics and Geotechnical Engineering; Chinese Academy of Sciences","keywords":"Boundary element method; Hydraulic fracturing; Inverse; Inverse problem; Linear elasticity; Regularization (linguistics); Boundary (topology); Finite element method; Geology; Computer science; Mechanics; Mathematical optimization; Geometry; Mathematics; Geotechnical engineering; Structural engineering; Mathematical analysis; Engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007060961,0.0001658083,0.0001983397,0.0001570772,0.0000914446,0.00001847629,0.0001330513,0.00002405234,0.00002412565],"category_scores_gemma":[0.00001780336,0.0001695444,0.0000656793,0.00006885656,0.000002676511,0.00009303191,0.0001508188,0.0001429985,0.000007451177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042342,"about_ca_system_score_gemma":0.000008210788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001713328,"about_ca_topic_score_gemma":0.000007375503,"domain_scores_codex":[0.9988535,0.00002283726,0.0005421226,0.0001376779,0.0001995123,0.000244371],"domain_scores_gemma":[0.9996186,0.00004093262,0.00009143294,0.00009545576,0.000008538354,0.0001449798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002822851,0.00001671776,0.0000215341,0.00004992534,0.0001007509,0.000005951546,0.001544562,0.2597425,0.7237708,0.000004229051,0.0001412684,0.01457356],"study_design_scores_gemma":[0.001250526,0.0003217004,0.0007737664,0.0003140935,0.00005800369,0.000006351516,0.0009676211,0.7744004,0.1859243,0.00001420821,0.03561204,0.0003569908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5498224,0.00007898646,0.4497637,0.00001861727,0.0001767529,0.00008818558,0.000008748541,0.0000197777,0.00002281853],"genre_scores_gemma":[0.9279166,0.0001056419,0.07173651,0.00003189683,0.0001118069,0.000005583138,0.0000120267,0.00005200378,0.00002786586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5378465,"threshold_uncertainty_score":0.6913819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02001529208924872,"score_gpt":0.2248323285368228,"score_spread":0.2048170364475741,"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."}}