{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001355959,0.0008671523,0.0007717292,0.003212532,0.000834683,0.002029774,0.002671855,0.0009546039,0.003599058],"category_scores_gemma":[0.006099993,0.0004348512,0.0008693596,0.003944773,0.0002651921,0.003336021,0.001676784,0.001308796,0.00289835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099265,"about_ca_system_score_gemma":0.00203181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01823253,"about_ca_topic_score_gemma":0.01848637,"domain_scores_codex":[0.9986854,0.0001345776,0.0002137472,0.0004736526,0.0003904868,0.0001021594],"domain_scores_gemma":[0.9974468,0.000294283,0.0002010722,0.0009658129,0.0009400634,0.0001520774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007272752,0.0008373458,0.05642139,0.001336708,0.000562814,0.001969297,0.0009886656,0.1813662,0.01711287,0.04505625,0.3353764,0.3582447],"study_design_scores_gemma":[0.00009866826,0.0001881445,0.01940112,0.0002022461,0.0001052277,0.0008278207,0.001439499,0.6597456,0.0209613,0.01794001,0.2789218,0.0001686158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06557384,0.0004617458,0.454161,0.0006963698,0.0004276095,0.001211881,0.4328926,0.0361281,0.008446728],"genre_scores_gemma":[0.1119201,0.0002780312,0.2035828,0.0001079417,0.00003993663,0.0007417531,0.6808655,0.0005557323,0.00190819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01823253,"threshold_uncertainty_score":0.0362528,"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."}}