{"id":"W3048519985","doi":"10.1155/2020/8810753","title":"Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Data mining; Computer science; Tensor decomposition; Inference; Missing data; Bayesian probability; Dirichlet process; Probabilistic logic; Nonparametric statistics; Artificial intelligence; Machine learning; Tensor (intrinsic definition); Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000351782,0.0001040583,0.0002268557,0.0001219196,0.0002517539,0.0000930332,0.0001859788,0.00005255626,0.00001936687],"category_scores_gemma":[0.0001685735,0.00009221681,0.00005212564,0.0005208527,0.0001083747,0.001286929,0.000002705255,0.0001810802,8.469561e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005427254,"about_ca_system_score_gemma":0.0001419292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001843323,"about_ca_topic_score_gemma":0.005849551,"domain_scores_codex":[0.9986489,0.0001213874,0.0004422723,0.0002230571,0.0004309937,0.0001334197],"domain_scores_gemma":[0.9987127,0.000351355,0.000410709,0.0001133038,0.0002586071,0.0001532796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003833902,0.000131002,0.008769929,0.00007815914,0.00008508682,0.00006199704,0.01672113,0.8080607,0.0002865103,0.00005977676,0.00001000003,0.1653523],"study_design_scores_gemma":[0.00767139,0.003238759,0.5027676,0.0004743263,0.002094042,0.00002378557,0.04985285,0.4273387,0.0003663875,0.00149256,0.003311972,0.001367626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.718017,0.00009011482,0.2797377,0.001906224,0.00003317602,0.0001403181,0.00005156777,0.00001616856,0.000007676936],"genre_scores_gemma":[0.9951228,0.0001278432,0.004113926,0.0002698309,0.0001453499,0.000001458593,0.0002065587,0.000009277971,0.000002904029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4939976,"threshold_uncertainty_score":0.3760493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01816090772753742,"score_gpt":0.3001213624816215,"score_spread":0.2819604547540841,"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."}}