{"id":"W4379983162","doi":"10.1109/icit58465.2023.10143102","title":"3D Multi-Views Object Classification Based on a Fully Generalized Dirichlet Allocation Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Latent Dirichlet allocation; Prior probability; Dirichlet distribution; Computer science; Topic model; Inference; Hierarchical Dirichlet process; Flexibility (engineering); Object (grammar); Generative model; Artificial intelligence; Machine learning; Theoretical computer science; Generative grammar; Mathematics; Bayesian probability; Statistics","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":[0.0004190838,0.0001651989,0.0001601431,0.0002498251,0.00012235,0.0001083582,0.0005568902,0.00007417986,0.00001020856],"category_scores_gemma":[0.0001280838,0.0001375089,0.00007669043,0.00106666,0.00002428048,0.0005074531,0.00009031703,0.0001096153,0.0002278976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007529875,"about_ca_system_score_gemma":0.00008481067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001267563,"about_ca_topic_score_gemma":0.000007744355,"domain_scores_codex":[0.9985762,0.00008288174,0.0002732495,0.0004934414,0.0003121848,0.0002620454],"domain_scores_gemma":[0.9988479,0.00006740232,0.00009755448,0.0007781037,0.0001358802,0.00007315582],"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":[0.0001014017,0.000610478,0.0003352989,0.00008204091,0.0000262399,0.00002354587,0.0006697008,0.07080142,0.1441992,0.1155203,0.04355218,0.6240782],"study_design_scores_gemma":[0.0003295292,0.00006914398,0.0006930536,0.00001354921,0.000003043621,5.37579e-7,0.000004573775,0.965304,0.02811716,0.001490267,0.003797462,0.0001776663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006146645,0.00002241846,0.993601,0.001235954,0.00006891553,0.000376606,0.000002542971,0.001744828,0.002333097],"genre_scores_gemma":[0.1542084,0.000109745,0.8394501,0.002401521,0.0000333495,0.0001623846,0.00004283853,0.00002014951,0.003571543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8945026,"threshold_uncertainty_score":0.5607449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1168574837901194,"score_gpt":0.3588048580031349,"score_spread":0.2419473742130156,"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."}}