{"id":"W3060860733","doi":"10.1109/jsen.2020.3017384","title":"Multi-Variate Data Fusion Technique for Reducing Sensor Errors in Intelligent Transportation Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sensor fusion; Computer science; Process (computing); Fusion; Intelligent transportation system; Real-time computing; Asynchronous communication; Data modeling; Data mining; Computation; Group method of data handling; Algorithm; Artificial intelligence; Machine learning; 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":[0.000434786,0.0001607936,0.00020359,0.0001918491,0.00006516303,0.00006017767,0.000256036,0.0001037201,0.00000657397],"category_scores_gemma":[0.00003313263,0.0001620473,0.00005701622,0.000189693,0.00001296052,0.0002505768,0.000009660425,0.0003548603,0.000004907275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000789663,"about_ca_system_score_gemma":0.00001437827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002118816,"about_ca_topic_score_gemma":0.00001582816,"domain_scores_codex":[0.9988212,0.00004413866,0.0005177977,0.0002236479,0.0001605126,0.0002326927],"domain_scores_gemma":[0.9995139,0.00002445103,0.00008732251,0.0002017964,0.00004464275,0.0001278749],"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":[0.00007662246,0.00005936595,0.0000793084,0.0003903859,0.00008117406,0.00009840562,0.001942756,0.735028,0.2235668,0.00004476215,0.03434778,0.004284563],"study_design_scores_gemma":[0.0005029389,0.00006082949,0.0002394316,0.000219575,0.000035865,0.00004196101,0.0005769515,0.9634172,0.02146393,0.000004764511,0.01321933,0.0002172572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03654218,0.0001131751,0.9599315,0.0002419512,0.00101072,0.0007956827,0.00009662873,0.001210109,0.00005803658],"genre_scores_gemma":[0.9627294,0.0004833313,0.03631134,0.00004972235,0.0002713834,0.00003166594,0.00005391854,0.00004689742,0.00002234262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9261872,"threshold_uncertainty_score":0.6608096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06034615539874806,"score_gpt":0.2804412878296976,"score_spread":0.2200951324309496,"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."}}