{"id":"W4302403691","doi":"10.48550/arxiv.1402.0266","title":"A stochastic domain decomposition method for time dependent mesh generation","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polygon mesh; Domain decomposition methods; Generator (circuit theory); Mesh generation; Domain (mathematical analysis); Grid; Decomposition; Computer science; Boundary (topology); Probabilistic logic; Algorithm; Time domain; Mathematical optimization; Applied mathematics; Topology (electrical circuits); Mathematics; Power (physics); Mathematical analysis; Geometry; Finite element method; Artificial intelligence; Engineering; Physics; Combinatorics; Computer graphics (images)","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":[0.000845382,0.00035079,0.0004265897,0.0004182828,0.0003249457,0.0005308423,0.001166475,0.0006632294,0.002705335],"category_scores_gemma":[0.001562458,0.0003310769,0.000551002,0.0003954643,0.0005889511,0.0004718945,0.001235013,0.001080782,0.0006822909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000444741,"about_ca_system_score_gemma":0.0006446264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008766777,"about_ca_topic_score_gemma":0.0009493451,"domain_scores_codex":[0.9995303,0.0001497113,0.00001894162,0.00006163619,0.0002077169,0.0000317645],"domain_scores_gemma":[0.9995214,0.0001975554,0.00003807292,0.00009010497,0.0001118199,0.00004108976],"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":[0.00008372446,0.00004967355,0.0005544475,0.00009956492,0.00003429654,0.0001288626,0.0001089578,0.6906509,0.0244288,0.2069427,0.002714639,0.07420338],"study_design_scores_gemma":[0.000009707487,0.00001015156,0.00002521052,0.000002735732,0.000001595254,0.00001789642,0.000002802371,0.9888466,0.001568909,0.008105787,0.001403835,0.000004791579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001661797,0.00001723624,0.9975709,0.00003080675,0.00001708795,0.00001625021,0.00001593407,0.00007882513,0.0005912212],"genre_scores_gemma":[0.1335533,0.00008365353,0.8623775,0.0001041477,0.00003633825,0.0002157497,0.0001930018,0.0002027117,0.00323371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002705335,"threshold_uncertainty_score":0.00905025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04990185611529672,"score_gpt":0.2308839840714659,"score_spread":0.1809821279561692,"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."}}