{"id":"W2950118368","doi":"10.48550/arxiv.1206.4658","title":"Dirichlet Process with Mixed Random Measures: A Nonparametric Topic\\n Model for Labeled Data","year":2012,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Dirichlet process; Nonparametric statistics; Random forest; Computer science; Measure (data warehouse); Mixture model; Pattern recognition (psychology); Dirichlet distribution; Artificial intelligence; Process (computing); Hierarchical Dirichlet process; Segmentation; Latent Dirichlet allocation; Mathematics; Data mining; Topic model; Statistics","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":[0.01251935,0.00132157,0.002340615,0.002978069,0.001410049,0.004316239,0.005683018,0.003469416,0.004208156],"category_scores_gemma":[0.03033152,0.001516757,0.002938793,0.004380379,0.002903427,0.006429318,0.003040007,0.004954646,0.002061882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002444273,"about_ca_system_score_gemma":0.002247158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004696996,"about_ca_topic_score_gemma":0.006733652,"domain_scores_codex":[0.9887351,0.007325363,0.0003803846,0.001873635,0.001292328,0.000393226],"domain_scores_gemma":[0.9841322,0.01130868,0.0008957321,0.002429879,0.0009429677,0.0002905048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002537788,0.0001836689,0.003520668,0.0003986544,0.0002549928,0.0002763963,0.0009396234,0.1752265,0.001994127,0.6887994,0.01174868,0.1164035],"study_design_scores_gemma":[0.00004438133,0.0000387516,0.0005641927,0.00006929904,0.00004146282,0.0001681597,0.00006714438,0.697539,0.0006326295,0.2904067,0.01037182,0.00005646881],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002013201,0.0003059063,0.9959952,0.00040425,0.0000510881,0.00006665605,0.000297972,0.000242405,0.0006233728],"genre_scores_gemma":[0.175935,0.001692498,0.8081536,0.0008308696,0.0009019236,0.001900983,0.003095993,0.0004579252,0.007031293],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01251935,"threshold_uncertainty_score":0.06620944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2036947857767872,"score_gpt":0.2498801374655578,"score_spread":0.04618535168877061,"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."}}