{"id":"W2535516716","doi":"10.1109/wcse.2013.17","title":"Evaluation of Stability and Similarity of Latent Dirichlet Allocation","year":2013,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pivotal (Canada)","funders":"","keywords":"Latent Dirichlet allocation; Divergence (linguistics); Computer science; Similarity (geometry); Stability (learning theory); Categorization; Artificial intelligence; Topic model; Matching (statistics); Set (abstract data type); Pattern recognition (psychology); Probabilistic latent semantic analysis; Dirichlet distribution; Kullback–Leibler divergence; Key (lock); Machine learning; Data mining; Mathematics; Image (mathematics); 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":[0.02070644,0.001067962,0.001511288,0.005223861,0.001246868,0.002725991,0.001504687,0.002045218,0.000711513],"category_scores_gemma":[0.08647685,0.0004183541,0.0009503369,0.002907433,0.001624014,0.003838297,0.002953153,0.001562837,0.0003283832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001831864,"about_ca_system_score_gemma":0.001247904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002606344,"about_ca_topic_score_gemma":0.002160951,"domain_scores_codex":[0.9846841,0.006924279,0.001678996,0.0024986,0.003776215,0.0004378437],"domain_scores_gemma":[0.9398124,0.03883971,0.003754491,0.006657146,0.009942033,0.0009941857],"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":[0.003226332,0.0005994994,0.1214757,0.0006037777,0.00112557,0.0003535086,0.001565089,0.4557206,0.01992826,0.0223768,0.003895498,0.3691293],"study_design_scores_gemma":[0.00006259752,0.0003067779,0.01265245,0.000037535,0.00006433531,0.0001985645,0.0003013264,0.9597103,0.01360939,0.01216854,0.0008261412,0.00006216401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5191549,0.001351698,0.4733484,0.0004039755,0.0001348366,0.0003289446,0.0006374652,0.001409078,0.003230643],"genre_scores_gemma":[0.8852395,0.0001577014,0.112228,0.00005317891,0.00005606916,0.0001912299,0.001375525,0.0002017079,0.0004969921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02070644,"threshold_uncertainty_score":0.1095074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09114239174412617,"score_gpt":0.2913854160830547,"score_spread":0.2002430243389285,"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."}}