{"id":"W2770641088","doi":"10.6084/m9.figshare.7859750","title":"Incremental Mixture Importance Sampling With Shotgun Optimization","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Shotgun; Sampling (signal processing); Computer science; Environmental science; Chemistry; Computer vision; Biochemistry","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":[0.004934898,0.002472563,0.002017173,0.003083323,0.0009836447,0.003014482,0.004939124,0.002825352,0.01581818],"category_scores_gemma":[0.01828633,0.00112898,0.002616408,0.003706332,0.0008452897,0.001908133,0.002781032,0.002880156,0.01263305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665922,"about_ca_system_score_gemma":0.002255183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01363098,"about_ca_topic_score_gemma":0.02872667,"domain_scores_codex":[0.9975185,0.001119238,0.0001480179,0.0007148202,0.0003738028,0.0001256245],"domain_scores_gemma":[0.9965699,0.00214139,0.000103994,0.0007222529,0.0003782308,0.00008423832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009876663,0.0003126744,0.005443239,0.001993057,0.0008306833,0.0002404447,0.0001933087,0.2380107,0.002194359,0.03456757,0.4907382,0.224488],"study_design_scores_gemma":[0.0004732367,0.00006515742,0.001519607,0.0001655559,0.0001345004,0.0002155209,0.00006139517,0.8042324,0.003681297,0.07872022,0.1106349,0.00009619728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.01054824,0.003484118,0.7996906,0.00157511,0.0005698381,0.001006057,0.1385069,0.03493496,0.009684208],"genre_scores_gemma":[0.05740626,0.0009832297,0.6648807,0.001058077,0.0001768556,0.002798241,0.2614372,0.003935055,0.007324387],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01581818,"threshold_uncertainty_score":0.05291706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034452851986339,"score_gpt":0.2837403723365439,"score_spread":0.2433958438166805,"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."}}