{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006273529,0.0005285938,0.0005120047,0.002076889,0.001029636,0.0003087084,0.001938024,0.0005034962,0.0004787305],"category_scores_gemma":[0.0002249011,0.0005727704,0.0001804073,0.002611443,0.0008413951,0.001743635,0.00005358904,0.003420527,0.0001565157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001643336,"about_ca_system_score_gemma":0.0002809729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001815502,"about_ca_topic_score_gemma":0.01736815,"domain_scores_codex":[0.9911538,0.0004770911,0.001229487,0.001328773,0.003880575,0.001930293],"domain_scores_gemma":[0.9953151,0.0005083472,0.0001111618,0.001685869,0.001544525,0.0008349835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001586013,0.001481528,0.02139697,0.003538224,0.0007780746,0.00090341,0.01751705,0.08232766,0.1875035,0.01343236,0.5127729,0.1567623],"study_design_scores_gemma":[0.0047512,0.0008852841,0.1798949,0.0008121937,0.0002234243,0.000006021034,0.01315181,0.3509026,0.01195369,0.000792924,0.4339555,0.002670466],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765531,0.0001661541,0.01057,0.0006883574,0.0009442628,0.00216315,0.001274512,0.004905004,0.002735446],"genre_scores_gemma":[0.9841616,0.0006216548,0.01197621,0.00005314542,0.0003982033,0.0002380598,0.002096622,0.0001916711,0.0002628948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2685749,"threshold_uncertainty_score":0.9996724,"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."}}