{"id":"W3143898298","doi":"10.1080/19427867.2021.1908490","title":"Developing extended trajectory database for heterogeneous traffic like NGSIM database","year":2021,"lang":"en","type":"article","venue":"Transportation Letters","topic":"Traffic control and management","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Federal Highway Administration","keywords":"Trajectory; Image stitching; Database; Computer science; Software deployment; Data mining; Automation; Traffic flow (computer networking); Real-time computing; Engineering; Computer network; Artificial intelligence; Software 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":[0.0000814196,0.0001922365,0.0001816178,0.0000843572,0.00006580916,0.00002983226,0.0001089126,0.00003484905,0.00006543855],"category_scores_gemma":[0.000004061375,0.0002232949,0.0001081315,0.0001457779,0.00001769536,0.0001831028,0.000002518272,0.00008759224,0.00001200254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005467815,"about_ca_system_score_gemma":0.0000311165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004409846,"about_ca_topic_score_gemma":0.0004857669,"domain_scores_codex":[0.9989238,0.00001398857,0.0003018075,0.0003015799,0.0001596018,0.0002991675],"domain_scores_gemma":[0.9995645,0.00004709853,0.00002819076,0.0002517303,0.00003304775,0.00007547597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006811499,0.00007441583,0.00001666488,0.0009120738,0.0003091589,0.0004242801,0.001018272,0.8743518,0.08404817,0.0007580232,0.007189673,0.03082933],"study_design_scores_gemma":[0.02198972,0.0001841807,0.04327669,0.0008201448,0.001825774,0.00008300691,0.001914768,0.2025807,0.0701438,0.00007475544,0.6514961,0.005610351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6400644,0.0004734766,0.3562348,0.0009638435,0.0007698235,0.0004263628,0.0004170965,0.0006266061,0.00002369411],"genre_scores_gemma":[0.9781311,0.000104311,0.01656292,0.002071521,0.0001074196,0.0001629145,0.002754616,0.00005829182,0.00004688774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6717711,"threshold_uncertainty_score":0.9105703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636091158701432,"score_gpt":0.2238204734372752,"score_spread":0.2074595618502609,"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."}}