{"id":"W3163694547","doi":"10.32473/flairs.v34i1.128506","title":"Covid-19 News Clustering using MCMC-Based Learing of finite EMSD Mixture Models","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain Monte Carlo; Cluster analysis; Computer science; Mixture model; Multinomial distribution; Artificial intelligence; Bayesian probability; Machine learning; Generative model; Flexibility (engineering); Task (project management); Dirichlet distribution; Statistical model; Data mining; Generative grammar; Mathematics; Statistics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002633718,0.0002354202,0.0003533245,0.0001655109,0.0003923948,0.0004660565,0.003197721,0.0001859592,0.00007660125],"category_scores_gemma":[0.002630155,0.0002033447,0.0004074457,0.001313945,0.0004629721,0.0008331793,0.00175898,0.0007945975,0.000003224197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362471,"about_ca_system_score_gemma":0.001863494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004262797,"about_ca_topic_score_gemma":0.00005312014,"domain_scores_codex":[0.9961603,0.0000967507,0.0007611437,0.0007261382,0.001702287,0.0005533959],"domain_scores_gemma":[0.9945044,0.0007019187,0.0003604879,0.0004356831,0.003716592,0.0002808969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000599197,0.000146006,0.0002303587,0.0003444693,0.0001116279,0.000004080975,0.005261139,0.02864238,0.2483098,0.6968657,0.0004942166,0.01953029],"study_design_scores_gemma":[0.00003970315,0.00002326621,0.000003949169,0.0001645565,0.000006341972,0.000005958861,0.0008005698,0.577907,0.2241479,0.196613,0.0001689203,0.0001188603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01303248,0.0001077558,0.9767389,0.006628909,0.0006120264,0.0002735477,0.00001665029,0.00004592222,0.002543858],"genre_scores_gemma":[0.737291,0.0001326183,0.2616813,0.0004519995,0.0001862293,0.00001894816,0.000002004617,0.00001687732,0.0002190057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7242585,"threshold_uncertainty_score":0.8292157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2869876506097959,"score_gpt":0.4139066479645853,"score_spread":0.1269189973547895,"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."}}