{"id":"W2896196150","doi":"10.1016/j.ijsolstr.2018.09.025","title":"Modeling nonplanar hydraulic fracture propagation using the XFEM: An implicit level-set algorithm and fracture tip asymptotics","year":2018,"lang":"en","type":"article","venue":"International Journal of Solids and Structures","topic":"Numerical methods in engineering","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; University of Pittsburgh; British Columbia Oil and Gas Commission; Schlumberger Foundation","keywords":"Fracture (geology); Level set (data structures); Set (abstract data type); Algorithm; Extended finite element method; Fracture mechanics; Level set method; Mathematics; Materials science; Structural engineering; Computer science; Finite element method; Engineering; Composite material; Artificial intelligence; Segmentation; Image segmentation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001975867,0.0001689828,0.000177778,0.0001201325,0.00009148267,0.0001458831,0.00023126,0.0001089558,0.00001303858],"category_scores_gemma":[0.00006178821,0.0001149159,0.00004037411,0.00006041654,0.00006641967,0.000243309,0.00003627341,0.0003814699,1.772706e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004436088,"about_ca_system_score_gemma":0.00001757258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002393582,"about_ca_topic_score_gemma":0.000002162599,"domain_scores_codex":[0.9990669,0.00003136415,0.0003092153,0.000114879,0.0003239511,0.0001536722],"domain_scores_gemma":[0.9994379,0.00005850249,0.00009182854,0.00008935144,0.0002292525,0.00009311485],"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":[0.00003200201,0.000006853491,0.000334083,0.00001833167,0.0002514899,0.00001867598,0.001321109,0.9262116,0.006869814,0.0001508403,0.00008685729,0.06469838],"study_design_scores_gemma":[0.0002603702,0.00008143341,0.002796266,0.00005996116,0.00003983222,0.0006946412,0.0001740965,0.9874272,0.002960031,0.0046361,0.0007123814,0.0001576599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.340937,0.0003323806,0.6579792,0.0001396072,0.0005271329,0.00004187443,0.00001208142,0.00001561223,0.00001510462],"genre_scores_gemma":[0.9138101,0.00008934339,0.08394212,0.0003756212,0.001750207,5.692733e-7,0.000003648654,0.00002694976,0.000001470686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5740371,"threshold_uncertainty_score":0.4686136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02764381221971738,"score_gpt":0.3112598201212409,"score_spread":0.2836160079015235,"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."}}