{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001119238,0.00002860589,0.00005936349,0.00001969506,0.000009590445,0.000009698791,0.0001100356,0.00001920508,0.00006644636],"category_scores_gemma":[0.00008837132,0.00002358921,0.00001022352,0.00006297675,0.00001712926,0.0002250597,0.00007154317,0.00001932349,0.000001250514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001701604,"about_ca_system_score_gemma":0.00004011768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003577174,"about_ca_topic_score_gemma":0.00002519196,"domain_scores_codex":[0.9992948,0.00007736198,0.000151117,0.0001169112,0.0003150333,0.00004482692],"domain_scores_gemma":[0.9991737,0.00003202928,0.0000503858,0.000257434,0.0004685757,0.00001785432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002809001,0.0003299321,0.1520148,0.0001235876,0.00004075958,5.313779e-8,0.003689083,0.005910272,0.05949453,0.09777466,0.0001628469,0.6804567],"study_design_scores_gemma":[0.00009258385,0.00001268328,0.1229847,0.000002637267,0.000005317691,1.107676e-7,0.00001032118,0.8505925,0.01417415,0.01210085,0.000001803281,0.00002230605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6815731,0.00002693707,0.3172982,0.0003580431,0.00002164867,0.0001332683,1.505471e-7,0.000009102582,0.0005795085],"genre_scores_gemma":[0.9699417,0.000002323141,0.03002041,0.00001995267,0.000003082352,0.000005907356,3.097344e-7,6.942369e-7,0.000005627845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8446823,"threshold_uncertainty_score":0.09619403,"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."}}