{"id":"W4393141805","doi":"10.1109/hpcc-dss-smartcity-dependsys60770.2023.00134","title":"Adaptive Priors for Burstiness Analysis in Topic Modeling with Generalized Dirichlet Distributions","year":2023,"lang":"en","type":"article","venue":"","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Burstiness; Dirichlet distribution; Prior probability; Computer science; Latent Dirichlet allocation; Applied mathematics; Artificial intelligence; Topic model; Mathematics; Bayesian probability; Mathematical analysis","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":[0.00007562724,0.00007699171,0.0001649292,0.00009320235,0.00009952974,0.00002922485,0.00007150835,0.00001952341,0.00003466018],"category_scores_gemma":[0.000003027824,0.00006315432,0.00008865685,0.001018929,0.00001646037,0.00005673159,0.00002188126,0.00004558611,0.000003304889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001982887,"about_ca_system_score_gemma":0.00003461211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004196,"about_ca_topic_score_gemma":0.0001916262,"domain_scores_codex":[0.9994445,0.00001210117,0.0001385135,0.0001585818,0.00006575796,0.0001805624],"domain_scores_gemma":[0.9997306,0.00003012568,0.00003147039,0.00009837851,0.00007481548,0.00003453903],"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":[0.00001595875,0.00003950895,0.09598816,0.000003249712,0.0002328767,4.629401e-7,0.0002637795,0.3752863,0.00001333635,0.5274256,0.00003679621,0.000693973],"study_design_scores_gemma":[0.0003430058,0.00001353434,0.00747858,0.000004956777,0.00006121836,1.019384e-8,0.0005710555,0.9847214,0.000007743324,0.00661798,0.00007250412,0.0001080611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5155844,0.000002137283,0.4836207,0.0001487938,0.00001934187,0.0001169837,0.00009466572,0.00002136591,0.000391622],"genre_scores_gemma":[0.9970375,0.000001446573,0.002009585,0.0000128547,0.00005303004,0.0001011012,0.0003216754,0.00000580636,0.0004569615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6094351,"threshold_uncertainty_score":0.2575359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517636864756973,"score_gpt":0.2980939865456189,"score_spread":0.2729176178980491,"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."}}