{"id":"W2144911699","doi":"10.1016/j.eswa.2005.01.019","title":"An ANN-based element extraction method for automatic mesh generation","year":2005,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadrilateral; Hexahedron; Computer science; Polygon mesh; Finite element method; Discretization; Artificial neural network; Tetrahedron; Mesh generation; Domain (mathematical analysis); Element (criminal law); Boundary (topology); Extended finite element method; Boundary element method; Algorithm; Artificial intelligence; Mathematics; Geometry; Structural engineering; Mathematical analysis; Engineering","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":[0.0004781896,0.000151584,0.0001449545,0.000165803,0.0003934888,0.0002621453,0.0003102459,0.00005765125,0.00001007312],"category_scores_gemma":[0.000007813339,0.0001352823,0.00004261347,0.0004155588,0.00001092049,0.0006344336,0.00001127143,0.00004925575,0.00002489496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001383715,"about_ca_system_score_gemma":0.0001542292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002969547,"about_ca_topic_score_gemma":0.00003655263,"domain_scores_codex":[0.9985766,0.0001053076,0.0003543301,0.000468733,0.0003028824,0.0001921801],"domain_scores_gemma":[0.9987174,0.0001191044,0.0001898443,0.0005687606,0.0002889391,0.0001159684],"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":[0.00001034907,0.0005066827,0.00002044238,0.00004576068,0.0000411127,3.498314e-7,0.0006527487,0.5205509,0.06062344,0.1174486,0.007222517,0.2928771],"study_design_scores_gemma":[0.0003551931,0.0001140638,0.00005413391,0.000008354064,0.000007394621,0.00001229179,0.00003610681,0.8837967,0.008713741,0.00007861177,0.1066599,0.0001635731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004679841,0.0002394618,0.9954819,0.00146055,0.000152843,0.001769434,0.00000891492,0.0003023857,0.0001165478],"genre_scores_gemma":[0.3175464,0.000002054328,0.6752706,0.0003374616,0.0008371506,0.005680112,0.0001847817,0.00001261481,0.0001288173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3632458,"threshold_uncertainty_score":0.5516653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02655501869568558,"score_gpt":0.3433390143874567,"score_spread":0.3167839956917712,"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."}}