{"id":"W2507984430","doi":"10.1109/ssp.2016.7551777","title":"Estimation of time-varying mixture models: An application to traffic estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Inference; Mixture model; Estimation; Data modeling; Data mining; Estimation theory; Algorithm; Artificial intelligence; Engineering","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":[0.0004143986,0.0001151314,0.0001474001,0.0001227761,0.00004301464,0.00004157699,0.0004377381,0.00007796853,0.00001194029],"category_scores_gemma":[0.00002950623,0.00007850416,0.000037617,0.0002919936,0.00001555065,0.00115806,0.00005791445,0.0000366671,0.00007156831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003219382,"about_ca_system_score_gemma":0.00003604578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008002458,"about_ca_topic_score_gemma":0.000001863862,"domain_scores_codex":[0.9989511,0.00007683376,0.0002488631,0.0003573301,0.0002077643,0.0001581391],"domain_scores_gemma":[0.9990677,0.00007276701,0.00009338697,0.0005703433,0.00008419164,0.0001116336],"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":[0.000004055247,0.00002938711,3.264645e-7,0.000006582693,0.000002516266,1.299901e-7,0.000279988,0.09332292,0.0146303,0.07732885,0.0001069593,0.814288],"study_design_scores_gemma":[0.0001359848,0.00006023674,0.0000120475,0.00002549359,0.000004423197,0.00000343403,8.737801e-7,0.9090557,0.01352545,0.07703751,0.00002615332,0.0001127007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005396855,0.00001296789,0.9912257,0.001164952,0.00004321761,0.0002918445,0.000002004877,0.0001891715,0.00167334],"genre_scores_gemma":[0.4078906,0.000001398588,0.5917773,0.0001114339,0.00001395439,0.00001192477,0.000002516363,0.000005822122,0.0001850468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8157328,"threshold_uncertainty_score":0.3201306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168446513445688,"score_gpt":0.2773139659137492,"score_spread":0.2604693145691804,"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."}}