{"id":"W202785208","doi":"","title":"Traffic Data Fusion Using SCAAT Kalman Filters","year":2010,"lang":"en","type":"article","venue":"Transportation Research Board 89th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fuse (electrical); Kalman filter; Computer science; Sensor fusion; Real-time computing; Data mining; Sampling (signal processing); Global Positioning System; Induction loop; Wireless sensor network; Categorical variable; Detector; Engineering; Artificial intelligence; Telecommunications; Computer network; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001000688,0.0009295965,0.0009741748,0.001310313,0.000783968,0.001311259,0.0009150351,0.0009491308,0.001063001],"category_scores_gemma":[0.00379089,0.0004768642,0.0008869878,0.001825144,0.0003755062,0.001868761,0.0008604385,0.00106943,0.000618304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200461,"about_ca_system_score_gemma":0.001514131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03800833,"about_ca_topic_score_gemma":0.02899397,"domain_scores_codex":[0.9989958,0.0001363015,0.00007239892,0.000330301,0.0003768691,0.00008824467],"domain_scores_gemma":[0.9988273,0.0002415038,0.0001375777,0.0001286057,0.0006392173,0.00002593587],"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":[0.0002722592,0.0001384861,0.004097527,0.0001131479,0.0001953503,0.00007536483,0.0002521674,0.6579121,0.01217579,0.007248398,0.003330378,0.314189],"study_design_scores_gemma":[0.000009396937,0.000030547,0.0008019998,0.000005494835,0.0000216709,0.00001371943,0.00001670661,0.9928825,0.002885737,0.001455734,0.001860278,0.00001623658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01564583,0.0001022603,0.9815015,0.00007935829,0.00007266814,0.00003547143,0.0001249458,0.001184992,0.001252947],"genre_scores_gemma":[0.6193383,0.0003743808,0.374521,0.0001136398,0.0001175535,0.000200759,0.001269338,0.0001128108,0.003952154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03800833,"threshold_uncertainty_score":0.07557422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07980650167287262,"score_gpt":0.3727675667259242,"score_spread":0.2929610650530516,"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."}}