{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":1,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"20e82de8ee5d","filters":{"venue":"2022 International Electrical Engineering Congress (iEECON)"}},"results":[{"id":"W4220926165","doi":"10.1109/ieecon53204.2022.9741682","title":"An Accelerated Nonparametric Bayesian Approach for Anomaly Detection with Feature Selection","year":2022,"lang":"en","type":"article","venue":"2022 International Electrical Engineering Congress (iEECON)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dirichlet process; Cluster analysis; Weighting; Feature selection; Computer science; Anomaly detection; Feature (linguistics); Pattern recognition (psychology); Dirichlet distribution; Artificial intelligence; Hierarchical Dirichlet process; Data mining; Nonparametric statistics; Latent Dirichlet allocation; Bounded function; Selection (genetic algorithm); Bayesian probability; Mathematics; Topic model; Statistics","authors":[{"name":"Yogesh Pawar","is_ca":true},{"name":"Manar Amayri","is_ca":false},{"name":"Nizar Bouguila","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008827537961830778,"gpt":0.2419421987132997,"spread":0.2331146607514689,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004443639,0.0002568349,0.0002510333,0.0006905986,0.0002834558,0.0002906888,0.0009916626,0.0001017765,0.00004594167],"category_scores_gemma":[0.00008349997,0.0002536303,0.0001030984,0.001813206,0.00001272984,0.0006059212,0.00009923501,0.0006418399,9.655736e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003934606,"about_ca_system_score_gemma":0.0001022249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001684132,"about_ca_topic_score_gemma":0.000002831413,"domain_scores_codex":[0.9980197,0.000101411,0.0002489861,0.0007072873,0.0005054016,0.0004172077],"domain_scores_gemma":[0.9990556,0.0001466812,0.000132523,0.0002850536,0.0002294736,0.0001506332],"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":[0.0006689143,0.00102564,0.0006689687,0.00007050761,0.0006905223,0.00005210788,0.0002436306,0.3894323,0.07550977,0.1027957,0.001731483,0.4271105],"study_design_scores_gemma":[0.0006968299,0.000649121,0.0004721443,0.000002739381,0.00001727753,0.0002077643,0.000002833539,0.9845458,0.008305822,0.0003219089,0.004444495,0.0003332929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004855911,0.00009991227,0.9929155,0.0001701597,0.0008456949,0.0004510589,0.00001662636,0.0003510363,0.0002940801],"genre_scores_gemma":[0.6650855,0.000004595403,0.3334604,0.0001670902,0.0001906579,0.0004593702,0.00005921933,0.00003699708,0.0005362129],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6602296,"threshold_uncertainty_score":0.9999916,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}