{"id":"W4289597284","doi":"10.48550/arxiv.2208.00692","title":"Stochastic Galerkin particle methods for kinetic equations of plasmas with uncertainties","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Gruppo Nazionale per la Fisica Matematica; Gruppo Nazionale per il Calcolo Scientifico; Banff International Research Station for Mathematical Innovation and Discovery; Ministero dell’Istruzione, dell’Università e della Ricerca; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Landau damping; Relaxation (psychology); Physics; Statistical physics; Solver; Instability; Shock (circulatory); Plasma; Mathematics; Applied mathematics; Classical mechanics; Mathematical optimization; Mechanics; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001314206,0.0001649369,0.0002683564,0.00005919809,0.0001130023,0.00001808647,0.0002441401,0.00003839462,0.001016819],"category_scores_gemma":[0.00002588589,0.0001703296,0.0001230998,0.0001810252,0.00006906201,0.00002921468,0.0002557532,0.0002022203,0.000004212052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004985915,"about_ca_system_score_gemma":0.0001074643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002355924,"about_ca_topic_score_gemma":0.000003701123,"domain_scores_codex":[0.9991218,0.00008719058,0.0001526773,0.0003735819,0.00004879324,0.0002159275],"domain_scores_gemma":[0.9987572,0.0005683975,0.0001780675,0.0003155519,0.0001024078,0.00007840961],"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":[0.00006829543,0.00006387365,0.0002390091,0.00002821607,0.0001153643,0.000001045219,0.0000614066,0.3494844,0.0000315868,0.6491804,0.00002483939,0.0007015947],"study_design_scores_gemma":[0.0005377741,0.0001472637,0.00005699854,0.00002692732,0.0002705695,6.993152e-8,0.0005285654,0.7813961,0.0001799145,0.2164586,0.0002171752,0.0001800641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05808537,0.00001306723,0.9405806,0.00002441255,0.0001569779,0.0003518656,0.0002603996,0.00001595622,0.0005113613],"genre_scores_gemma":[0.9862114,0.000001728662,0.01282301,0.000006106306,0.00003915089,0.00001609824,0.00007142487,0.00001998867,0.0008111172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.928126,"threshold_uncertainty_score":0.9998964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09401581636039866,"score_gpt":0.2565807317614547,"score_spread":0.1625649154010561,"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."}}