{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000253257,0.000103625,0.0001017576,0.00005414969,0.00004209691,0.00004034394,0.0004742838,0.0000545674,0.0001012882],"category_scores_gemma":[0.00002984939,0.00007473023,0.00004540665,0.0001389547,0.00001557833,0.0001058886,0.00008034526,0.00009657186,0.00005528746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001533139,"about_ca_system_score_gemma":0.00002638136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002291245,"about_ca_topic_score_gemma":0.00003625718,"domain_scores_codex":[0.9992372,0.00007398939,0.0001025382,0.0002798334,0.0001213431,0.0001850668],"domain_scores_gemma":[0.9992946,0.00004984434,0.00002685717,0.000524134,0.00002655894,0.00007803733],"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":[0.00002607283,0.0002351036,0.0001484311,0.00006527582,0.0000103559,0.00006075912,0.001123167,0.0001268052,0.0004135136,0.7870159,0.004418146,0.2063565],"study_design_scores_gemma":[0.0002923267,0.0002004559,0.00106122,0.00004112142,0.000005510695,0.000008790011,0.000002923259,0.9331301,0.001991407,0.06075303,0.002234289,0.0002788489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001342292,0.00001001662,0.8464985,0.0002786297,0.0001671838,0.0001046576,4.387168e-7,0.000165195,0.1526411],"genre_scores_gemma":[0.1795393,5.361226e-7,0.8176981,0.001793239,0.00003509685,0.00001175355,3.916916e-7,0.000006929726,0.0009146055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9330033,"threshold_uncertainty_score":0.3047411,"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."}}