{"id":"W2085659772","doi":"10.3141/2241-10","title":"Automated Detection of Spatial Traffic Violations through use of Video Sensors","year":2011,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Carleton University","funders":"","keywords":"Longest common subsequence problem; Cluster analysis; Computer science; Intersection (aeronautics); Similarity (geometry); Matching (statistics); Data mining; Piecewise; Similarity measure; Artificial intelligence; Measure (data warehouse); Pattern recognition (psychology); Image (mathematics); Mathematics; Algorithm; Engineering; Transport engineering; Statistics","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":[0.0003183696,0.0003496535,0.0002771113,0.002087625,0.0002443332,0.0005084249,0.0004099053,0.000277586,0.0005398523],"category_scores_gemma":[0.001275604,0.0001382811,0.0001390297,0.001035164,0.0001770845,0.0004337758,0.0003452367,0.0002377998,0.0002644579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003475936,"about_ca_system_score_gemma":0.000509491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01165712,"about_ca_topic_score_gemma":0.01927241,"domain_scores_codex":[0.9996567,0.00006666061,0.00001795536,0.00006446836,0.0001455144,0.00004874879],"domain_scores_gemma":[0.9992317,0.0001321195,0.0001976376,0.00005878242,0.0003458926,0.00003391294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006130679,0.0002871973,0.1290184,0.0003412561,0.0001167277,0.0004227233,0.0006527574,0.03729949,0.2553194,0.001293808,0.002491864,0.5721433],"study_design_scores_gemma":[0.00002585579,0.000366439,0.195329,0.00005649449,0.00006563945,0.0003821259,0.0008282845,0.7073952,0.09142677,0.0009210738,0.003136618,0.0000664512],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8766308,0.0002485643,0.1166466,0.0001025095,0.0000423451,0.0001508273,0.0009889492,0.001243439,0.003946089],"genre_scores_gemma":[0.9553543,0.0001021757,0.04356726,0.00001807043,0.00001019683,0.00003569747,0.0003715389,0.00001327088,0.0005274294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01165712,"threshold_uncertainty_score":0.02317852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058408264743431,"score_gpt":0.3314524149391158,"score_spread":0.2256115884647727,"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."}}