{"id":"W3185361098","doi":"10.1007/978-3-030-80387-2_10","title":"GPU Accelerated PMCMC Algorithm with System Dynamics Modelling","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Mental Health Research Topics","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Markov chain Monte Carlo; Particle filter; Speedup; Monte Carlo method; Markov chain; Algorithm; Acceleration; General-purpose computing on graphics processing units; Bayesian probability; Theoretical computer science; Parallel computing; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002303951,0.0005030456,0.0008422045,0.0003390767,0.0005313093,0.0009233438,0.00157501,0.001423368,0.01219664],"category_scores_gemma":[0.001437471,0.00048242,0.0006041474,0.0006479025,0.0003247416,0.000594947,0.00100654,0.001472129,0.003007678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004835413,"about_ca_system_score_gemma":0.001489013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01177921,"about_ca_topic_score_gemma":0.01127963,"domain_scores_codex":[0.9998449,0.00002925847,0.000005856284,0.00002223865,0.00008068061,0.0000170262],"domain_scores_gemma":[0.9996046,0.0001718857,0.00002324153,0.00005274533,0.0001183718,0.00002921997],"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":[0.00007166903,0.00003926779,0.0002857083,0.0001279053,0.00004073982,0.0001443713,0.00005922229,0.906705,0.002040285,0.01648811,0.008424072,0.06557366],"study_design_scores_gemma":[0.000005480765,0.000004241508,0.00002192772,0.000004308002,0.000002189931,0.00001474789,0.000003203512,0.9963784,0.0002627566,0.001198722,0.002102001,0.00000213932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005647193,0.0005504948,0.9742618,0.0002983111,0.0003250944,0.00005997216,0.0001891437,0.001173226,0.01749471],"genre_scores_gemma":[0.2058985,0.0005138987,0.7638046,0.000297635,0.0002114405,0.0003371204,0.0006084382,0.0008901766,0.02743823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01219664,"threshold_uncertainty_score":0.04080176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06867399086443807,"score_gpt":0.3466020641993704,"score_spread":0.2779280733349323,"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."}}