{"id":"W3200993033","doi":"10.48550/arxiv.2109.08357","title":"Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Missing data; Data mining; Imputation (statistics); Graph; Deep learning; Artificial intelligence; Convolutional neural network; Big data; Intelligent transportation system; Machine learning; Theoretical computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001085664,0.0002823863,0.0002549832,0.0001967566,0.0001143409,0.000106115,0.0005012195,0.0001915792,0.00001571669],"category_scores_gemma":[0.000004398779,0.0003426475,0.0001010501,0.0002067954,0.00006310143,0.0002974447,0.0003090035,0.0003485159,6.195227e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001546952,"about_ca_system_score_gemma":0.00004083893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003324654,"about_ca_topic_score_gemma":0.0003557195,"domain_scores_codex":[0.9987884,0.00004001837,0.0001941725,0.0006429101,0.00007317447,0.0002613676],"domain_scores_gemma":[0.9990652,0.00004108267,0.0001058325,0.0006153427,0.00008291681,0.00008964854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003019686,0.00003172576,0.0003885217,0.0002040252,0.0001779865,0.0000534984,0.00002850357,0.993428,0.000003611802,0.0004850004,0.001447035,0.003721894],"study_design_scores_gemma":[0.0005293665,0.00003117593,0.00475055,0.0001075301,0.0001723376,0.000005776953,0.0001065953,0.9937319,0.000002100415,0.0000946275,0.0001230639,0.0003450234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1688766,0.00006892077,0.8278875,0.00003907844,0.0003522222,0.0004440731,0.0002450481,0.002030417,0.00005609772],"genre_scores_gemma":[0.9888581,0.0001223822,0.00353063,0.00003856247,0.00005401124,0.00000376012,0.007320392,0.00004711744,0.00002502406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8243569,"threshold_uncertainty_score":0.9999025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06591026237124424,"score_gpt":0.2006809423428645,"score_spread":0.1347706799716202,"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."}}