{"id":"W2726627169","doi":"10.1016/j.cpc.2017.06.004","title":"Massively parallel microscopic particle-in-cell","year":2017,"lang":"en","type":"article","venue":"Computer Physics Communications","topic":"Strong Light-Matter Interactions","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"University of Toronto; Bundesministerium für Bildung und Forschung; Fonds de recherche du Québec – Nature et technologies; Université de Montréal; Deutsche Forschungsgemeinschaft; Government of Ontario; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Canada Foundation for Innovation; Ontario Research Foundation; Natural Sciences and Engineering Research Council of Canada; International Business Machines Corporation","keywords":"Massively parallel; Computation; IBM; Particle (ecology); Physics; Computational science; Computer science; Collision; Computational physics; Parallel computing; Algorithm; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00004963536,0.0001521066,0.0001771073,0.00002435335,0.0006983177,0.0003105913,0.001667877,0.00002263665,0.00006021321],"category_scores_gemma":[0.000001751063,0.0001666378,0.0001028676,0.000060268,0.0001611521,0.0003943364,0.0008334549,0.0002959366,0.0004233751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003227621,"about_ca_system_score_gemma":0.00004169391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003241021,"about_ca_topic_score_gemma":0.00002463569,"domain_scores_codex":[0.9991443,0.00006576416,0.0002425773,0.0002154431,0.00008020383,0.0002516715],"domain_scores_gemma":[0.9964025,0.0001198391,0.000215706,0.003128175,0.00006716519,0.0000666144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001495953,0.00370844,0.7279112,0.00003378179,0.000254477,0.000003772165,0.002973564,0.001651623,0.0242844,0.1734177,0.01873541,0.04701071],"study_design_scores_gemma":[0.006011765,0.0001422894,0.6375849,0.0004075166,0.0001686183,0.000003402616,0.0005284465,0.1817738,0.03476387,0.1008474,0.03585637,0.001911692],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5954161,0.0001619529,0.2911218,0.01499105,0.00134974,0.001031361,0.0001387938,0.0002035137,0.09558579],"genre_scores_gemma":[0.9472905,0.000001194375,0.0516868,0.000120722,0.0002657754,0.00009106341,0.00004499619,0.00002041897,0.0004784685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3518745,"threshold_uncertainty_score":0.6795292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04186211819767234,"score_gpt":0.3184110726309039,"score_spread":0.2765489544332316,"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."}}