{"id":"W2291161277","doi":"","title":"Large-Scale Multi-Sensor Monitoring of Pedestrian Dynamics in Public Spaces: Preliminary Results","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; Extrapolation; Attendance; Scale (ratio); Computer science; Process (computing); Real-time computing; Data science; Transport engineering; Geography; Engineering; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01902531,0.0004580299,0.0007423515,0.002497469,0.0005761268,0.0002574751,0.00199693,0.0004332733,0.0000288871],"category_scores_gemma":[0.001851682,0.0003943518,0.0002698804,0.004652081,0.0006955777,0.002121417,0.00006079501,0.001452526,0.00005290234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005215259,"about_ca_system_score_gemma":0.0009914786,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004028148,"about_ca_topic_score_gemma":0.03281182,"domain_scores_codex":[0.9864964,0.002835388,0.002001966,0.001695417,0.004294571,0.002676204],"domain_scores_gemma":[0.9896902,0.003650711,0.0004151878,0.001361103,0.004124347,0.0007584388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00194286,0.0009608581,0.9552011,0.0004987746,0.00007160565,0.0003238162,0.01428403,0.0002799201,0.002985554,0.006703594,0.0002362253,0.01651169],"study_design_scores_gemma":[0.005320273,0.001053449,0.968106,0.0008266715,0.000009485996,7.305019e-7,0.01325361,0.002953378,0.004472269,0.001356529,0.002109175,0.0005384053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9035109,0.0001961025,0.08743312,0.005429684,0.0004010141,0.001466953,0.0007473181,0.0002893877,0.0005255201],"genre_scores_gemma":[0.9296576,0.0006495469,0.06862383,0.000013938,0.0001308997,0.0002631439,0.0001162449,0.00007218886,0.0004725452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02878367,"threshold_uncertainty_score":0.9998508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058100559642292,"score_gpt":0.4015172574616747,"score_spread":0.2957072014974454,"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."}}