{"id":"W4389072935","doi":"10.48550/arxiv.2311.14312","title":"An Adaptive Fast-Multipole-Accelerated Hybrid Boundary Integral Equation Method for Accurate Diffusion Curves","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Research Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Discretization; Fast multipole method; Partial differential equation; Boundary element method; Mathematics; Boundary (topology); Diffusion equation; Computer science; Mathematical analysis; Applied mathematics; Algorithm; Multipole expansion; Finite element method","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004376015,0.0004901671,0.0006023321,0.000450978,0.000433892,0.000890099,0.001305074,0.001237604,0.003192347],"category_scores_gemma":[0.001567133,0.0003322965,0.0005634175,0.0004551775,0.0004928835,0.001094062,0.00115193,0.001301976,0.001000711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004523281,"about_ca_system_score_gemma":0.0007699897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002321891,"about_ca_topic_score_gemma":0.002280201,"domain_scores_codex":[0.9998135,0.00003147198,0.00000735125,0.00002039268,0.0001123459,0.00001485713],"domain_scores_gemma":[0.9996721,0.0001294969,0.00002310828,0.00003477632,0.0001126094,0.00002793244],"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":[0.0001031436,0.000123653,0.000931601,0.0002218096,0.00005491555,0.0002925244,0.0003732719,0.6521907,0.06384559,0.1183193,0.006393809,0.1571498],"study_design_scores_gemma":[0.000006006178,0.000004539339,0.00002172254,0.0000048307,0.00000156655,0.00002206346,0.000005310098,0.9944569,0.0008898118,0.002304053,0.002278709,0.000004379709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004411043,0.0001289383,0.9929606,0.0001116486,0.0000502111,0.00002496225,0.00002514138,0.0002176944,0.002069761],"genre_scores_gemma":[0.1037843,0.0002589171,0.889651,0.00013891,0.0000566836,0.0001448254,0.0001183448,0.0003410208,0.005506077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003192347,"threshold_uncertainty_score":0.01067942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183102237242199,"score_gpt":0.259034768415829,"score_spread":0.1407245446916091,"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."}}