{"id":"W2543456117","doi":"10.1109/icmla.2011.6174513","title":"Probabilistic clustering based on Langevin mixture","year":2011,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hypersphere; Cluster analysis; Computer science; Probabilistic logic; Artificial intelligence; Mixture model; Representation (politics); Categorization; Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004003194,0.0008494346,0.002088035,0.002939636,0.001284244,0.002266457,0.003355212,0.002229857,0.002058053],"category_scores_gemma":[0.01170987,0.001195076,0.001788236,0.001898395,0.002891227,0.003997866,0.002698323,0.001925638,0.000836117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527383,"about_ca_system_score_gemma":0.001327528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003890858,"about_ca_topic_score_gemma":0.003396812,"domain_scores_codex":[0.9976625,0.0008405523,0.0001102235,0.0004794674,0.0007309366,0.0001762599],"domain_scores_gemma":[0.9958513,0.002193705,0.0005343408,0.0005146901,0.000688782,0.0002172288],"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":[0.00005924284,0.00002571013,0.0008807867,0.0000818627,0.00007238434,0.0000699494,0.0001858706,0.6375949,0.002194976,0.3391941,0.001082116,0.01855812],"study_design_scores_gemma":[0.000003989599,0.000006633146,0.00008265691,0.000005765417,0.000003639562,0.0000155017,0.00000789869,0.9621438,0.0002439961,0.03706219,0.0004041118,0.00001973544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00582013,0.0001195583,0.9930685,0.0001398585,0.00002263712,0.00002033122,0.00003114035,0.0001393458,0.0006385482],"genre_scores_gemma":[0.5089734,0.0007426438,0.48057,0.0003658816,0.0001710864,0.0004436717,0.0006731168,0.0004166464,0.007643578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004003194,"threshold_uncertainty_score":0.02117115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0419371948556178,"score_gpt":0.2512315649239952,"score_spread":0.2092943700683774,"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."}}