{"id":"W2759089887","doi":"10.1109/tits.2017.2747516","title":"Automated Analysis of Pedestrian Group Behavior in Urban Settings","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Schema crosswalk; Computer science; Similarity (geometry); Artificial intelligence; Similarity measure; Measure (data warehouse); Computer vision; Trajectory; Movement (music); Simulation; Data mining; Transport engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000267388,0.000315776,0.0004157491,0.00219586,0.000290173,0.0003599797,0.0003060216,0.0002404777,0.0005618931],"category_scores_gemma":[0.0008091034,0.0001624041,0.0001791417,0.001136748,0.0001669166,0.0003005559,0.0004289252,0.0001420204,0.0003187316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002847383,"about_ca_system_score_gemma":0.0003041554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009185163,"about_ca_topic_score_gemma":0.01222386,"domain_scores_codex":[0.9997066,0.000081254,0.00001241948,0.00007671347,0.00007743801,0.00004552394],"domain_scores_gemma":[0.9996437,0.00008660951,0.00007960312,0.00004693517,0.0001008369,0.00004234332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006548099,0.0004189564,0.2928818,0.0002808141,0.00013436,0.000820248,0.001445926,0.1221377,0.04822225,0.001897186,0.003651469,0.5274546],"study_design_scores_gemma":[0.00001810198,0.0002326639,0.2706809,0.00002408,0.00003739464,0.0003221119,0.001050198,0.7148476,0.009166848,0.001667737,0.001919482,0.00003292831],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8998012,0.0001298656,0.09702547,0.00003415735,0.00001156421,0.00008742297,0.0006438575,0.0007791367,0.0014873],"genre_scores_gemma":[0.9721746,0.00005590272,0.02672378,0.000005123539,0.000005251141,0.00002581786,0.0006329102,0.0000164102,0.0003602219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009185163,"threshold_uncertainty_score":0.0182634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047988296285317,"score_gpt":0.2729558448255132,"score_spread":0.25247596186266,"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."}}