{"id":"W2343666936","doi":"10.6084/m9.figshare.7910549.v3","title":"Adaptive Incremental Mixture Markov Chain Monte Carlo","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Markov chain Monte Carlo; Monte Carlo method; Markov chain; Computer science; Statistical physics; Mathematics; Statistics; Machine learning; Physics","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":[0.003735619,0.001004516,0.001608355,0.001501517,0.0007036157,0.001658015,0.004305758,0.001632581,0.005383125],"category_scores_gemma":[0.01546487,0.0008473477,0.001501433,0.001766678,0.0009308556,0.001979176,0.002044018,0.002792788,0.002282069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001341572,"about_ca_system_score_gemma":0.002276304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009467272,"about_ca_topic_score_gemma":0.02328942,"domain_scores_codex":[0.9983783,0.0008078996,0.00007512459,0.0003459814,0.000288013,0.0001046736],"domain_scores_gemma":[0.994786,0.003440684,0.0002570003,0.0008385316,0.0005257886,0.000152036],"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":[0.0003826591,0.0001781014,0.006108278,0.0002835073,0.0002661443,0.0002220676,0.0001645985,0.7618812,0.00121425,0.07843128,0.02845844,0.1224095],"study_design_scores_gemma":[0.00003886593,0.000007814021,0.0001557409,0.00001290887,0.00001135478,0.00002673439,0.000005176039,0.970051,0.0003203716,0.02613053,0.003227407,0.00001206656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.008275433,0.0005710021,0.9825639,0.0004044091,0.00008632636,0.0001824581,0.00278577,0.003274471,0.001856274],"genre_scores_gemma":[0.1772144,0.00055833,0.8018578,0.0004508129,0.0001461365,0.0009052462,0.01482885,0.0008265904,0.003211788],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.009467272,"threshold_uncertainty_score":0.01975608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029195418842652,"score_gpt":0.3343484373503098,"score_spread":0.2314288954660446,"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."}}