{"id":"W2166217501","doi":"10.1002/num.10032","title":"A domain decomposition preconditioner for hermite collocation problems","year":2003,"lang":"en","type":"article","venue":"Numerical Methods for Partial Differential Equations","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Preconditioner; Mathematics; Domain decomposition methods; Piecewise; Discretization; Hermite polynomials; Collocation (remote sensing); Partial differential equation; Applied mathematics; Collocation method; Generalized minimal residual method; Multigrid method; Boundary (topology); Linear system; Finite element method; Mathematical analysis; Computer science; Differential equation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005221469,0.0002817952,0.0004156789,0.0001231364,0.000297481,0.00006978775,0.0001415682,0.0001466196,0.0001116882],"category_scores_gemma":[0.001224985,0.0002896284,0.0002528662,0.0003757581,0.00006287511,0.0001988759,0.00001368076,0.0001444212,0.00001410927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001488842,"about_ca_system_score_gemma":0.00003544156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.375656e-7,"about_ca_topic_score_gemma":5.707871e-7,"domain_scores_codex":[0.9979731,0.000393716,0.0006863278,0.0003508119,0.0001765983,0.0004194641],"domain_scores_gemma":[0.9952282,0.004009616,0.0001483818,0.0002349482,0.0002098284,0.0001689894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000677388,0.0004227356,0.00000505571,0.0004088795,0.0002297218,1.420427e-7,0.0003854529,0.2435147,0.07650167,0.5683749,0.0002611075,0.1098279],"study_design_scores_gemma":[0.0007215788,0.0001502255,0.00002368706,0.00002434288,0.00008850758,0.000002628962,0.00002235811,0.5895097,0.0198798,0.3773799,0.01189088,0.0003064033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005504889,0.00008581048,0.9954644,0.00007503572,0.001131443,0.001960992,0.00009674017,0.0003489492,0.0002861466],"genre_scores_gemma":[0.07105035,0.000004136862,0.924772,0.00004615945,0.000188614,0.003593737,0.0002006855,0.0000860849,0.00005825974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.345995,"threshold_uncertainty_score":0.9999556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05283478246619061,"score_gpt":0.4044903603816368,"score_spread":0.3516555779154462,"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."}}