{"id":"W2973408110","doi":"10.1139/cjce-2019-0087","title":"Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Headway; Computer science; Data collection; Minimum bounding box; Ground truth; Data mining; Artificial intelligence; Segmentation; Traffic flow (computer networking); Sensor fusion; Computer vision; Real-time computing; Simulation; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237121,0.0001160055,0.0002243957,0.0003452099,0.00009848695,0.0002045673,0.0005351062,0.00005666281,0.000005911799],"category_scores_gemma":[0.0002552033,0.0001233822,0.00005403743,0.0002910953,0.000006923122,0.0007256418,0.0000191364,0.0001783147,7.407966e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008666822,"about_ca_system_score_gemma":0.0006084332,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001597764,"about_ca_topic_score_gemma":0.01893044,"domain_scores_codex":[0.9990165,0.00002987926,0.0003103045,0.0002116818,0.0001258537,0.0003057465],"domain_scores_gemma":[0.9989425,0.0002286692,0.00008364358,0.0003311535,0.000143756,0.0002702757],"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":[0.000005967572,0.000003786712,0.0007633,0.00009491295,0.00002577594,0.00001438116,0.0003325887,0.9855925,0.003970976,0.000118066,0.0000403288,0.009037363],"study_design_scores_gemma":[0.0005690766,0.00006389547,0.0005662458,0.0001869662,0.000009213755,0.00004006355,0.00002088431,0.997022,0.000691393,0.00005191713,0.0006369699,0.0001413763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2171206,0.0006253053,0.7813222,0.0001907949,0.0006003529,0.00009531903,0.000003000392,0.00002180873,0.00002060692],"genre_scores_gemma":[0.9523177,0.000007491565,0.0474834,0.00006173587,0.000102443,0.000001533094,0.000001916002,0.00001812542,0.000005709499],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7351971,"threshold_uncertainty_score":0.9989715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536193240355473,"score_gpt":0.2493721927619699,"score_spread":0.2140102603584151,"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."}}