{"id":"W4387959888","doi":"10.1063/5.0160853","title":"Direct implicit and explicit energy-conserving particle-in-cell methods for modeling of capacitively coupled plasma devices","year":2023,"lang":"en","type":"article","venue":"Physics of Plasmas","topic":"Plasma Diagnostics and Applications","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Princeton Plasma Physics Laboratory; U.S. Department of Energy; Laboratory Directed Research and Development; Lawrence Berkeley National Laboratory; Office of Science; National Energy Research Scientific Computing Center","keywords":"Particle-in-cell; Plasma; Physics; Computational physics; Debye length; Electron; Kinetic energy; Momentum (technical analysis); Debye; Mechanics; Statistical physics; Classical mechanics; Quantum mechanics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001929576,0.0001429125,0.0003095538,0.00006675007,0.00003913641,0.00001277853,0.0001219847,0.0000504534,7.455344e-7],"category_scores_gemma":[0.00005312239,0.0001592439,0.00005926501,0.0003345307,0.0000240249,0.00008661448,0.00004327125,0.00005736202,0.000001751642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001493698,"about_ca_system_score_gemma":0.00001550757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001143925,"about_ca_topic_score_gemma":0.0000692488,"domain_scores_codex":[0.9991735,0.00001534649,0.0003122471,0.0001793661,0.00007592839,0.0002436024],"domain_scores_gemma":[0.9983583,0.001297768,0.00006666233,0.0001569653,0.000065431,0.00005488401],"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":[0.00001423583,0.00005136148,0.0003495696,0.0003433657,0.00005728659,5.672674e-7,0.0005082112,0.6079212,0.3661882,0.01979451,0.00003582074,0.004735649],"study_design_scores_gemma":[0.0002621408,0.00001229453,0.00006345251,0.00003906082,0.00002103415,2.163771e-7,0.000111664,0.6316586,0.3633189,0.004370856,0.00004524258,0.00009652573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023073,0.00006768765,0.09666579,0.00002892892,0.0000389827,0.00015116,0.0001059905,0.00007777604,0.0005564046],"genre_scores_gemma":[0.9781911,0.0001658449,0.02143461,0.000007009376,0.00002412156,0.0001139152,0.00001993505,0.00003470972,0.000008804675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07588378,"threshold_uncertainty_score":0.6493776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331372105986294,"score_gpt":0.2872863800808201,"score_spread":0.2541491694821907,"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."}}