{"id":"W2780489948","doi":"","title":"Ottawa Bayesian inference workshop","year":2017,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Inference; Bayesian probability; Bayesian inference; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004941257,0.002244259,0.004132471,0.00257484,0.002391658,0.006532624,0.003718662,0.003644956,0.09823086],"category_scores_gemma":[0.01497054,0.002472897,0.002385936,0.004095893,0.002975959,0.004472769,0.004031469,0.005330587,0.04520667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006115786,"about_ca_system_score_gemma":0.006979265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09119952,"about_ca_topic_score_gemma":0.143777,"domain_scores_codex":[0.9961409,0.001476642,0.0002299578,0.0009730963,0.0009698439,0.0002096104],"domain_scores_gemma":[0.9920855,0.003341172,0.0001982991,0.002432216,0.001613065,0.0003296962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003006838,0.0001003686,0.0006883218,0.0003320121,0.0003011046,0.0001581082,0.0001717843,0.01764887,0.0004988044,0.1537889,0.5188277,0.3071834],"study_design_scores_gemma":[0.0000828245,0.00003231074,0.00107489,0.0003574298,0.0001471405,0.0001430576,0.0001312184,0.04870953,0.001276838,0.3834978,0.5644157,0.0001311539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005914531,0.0606976,0.5389841,0.04202093,0.01108568,0.0001956328,0.01779577,0.008096203,0.3152095],"genre_scores_gemma":[0.09187765,0.02637262,0.2700395,0.00373503,0.004253095,0.0004629423,0.01740094,0.005643822,0.5802145],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09823086,"threshold_uncertainty_score":0.328615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02584079401184276,"score_gpt":0.3053321987844826,"score_spread":0.2794914047726398,"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."}}