{"id":"W2033456925","doi":"10.1002/nme.308","title":"An efficient multi‐layer planar 3D fracture growth algorithm using a fixed mesh approach","year":2001,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Numerical methods in engineering","field":"Engineering","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of Minnesota","keywords":"Planar; Discontinuity (linguistics); Algorithm; Fracture (geology); Displacement (psychology); Surface (topology); Mathematics; Geometry; Computer science; Mathematical analysis; Materials science; Composite material; Computer graphics (images)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004859295,0.0004740967,0.0005840957,0.0004977282,0.0004265523,0.0006609943,0.002056862,0.001138347,0.002421444],"category_scores_gemma":[0.001113711,0.0004282368,0.000813584,0.0003792358,0.0004315571,0.0006036873,0.001009902,0.0006409441,0.0005559078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007329373,"about_ca_system_score_gemma":0.0009254994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005645403,"about_ca_topic_score_gemma":0.003884115,"domain_scores_codex":[0.9998216,0.00003209462,0.00001018959,0.0000232033,0.00009213949,0.00002083985],"domain_scores_gemma":[0.999615,0.0001739611,0.00003756751,0.00005072206,0.00009880367,0.00002395945],"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":[0.00004194218,0.00002195412,0.0005112046,0.00005364967,0.00002100827,0.00005623649,0.00005978197,0.9374472,0.007211199,0.009227839,0.0005828059,0.04476506],"study_design_scores_gemma":[0.000006427853,0.000006324747,0.0000246488,0.000002024733,0.000001502477,0.000008219357,0.000003355186,0.998287,0.0006689317,0.0006153987,0.0003731525,0.000002919594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02334001,0.00006536801,0.9735963,0.00007547715,0.00002023115,0.00004588223,0.00007184656,0.0006836487,0.002101291],"genre_scores_gemma":[0.2602995,0.00008156084,0.7369022,0.00005069235,0.000008512106,0.0001896377,0.000221863,0.0002073136,0.002038648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005645403,"threshold_uncertainty_score":0.01122504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04199569458642483,"score_gpt":0.3854371690418181,"score_spread":0.3434414744553932,"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."}}