{"id":"W2802944295","doi":"10.1155/2018/7328074","title":"Monitoring the Number of Pedestrians in an Area: The Applicability of Counting Systems for Density State Estimation","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Density estimation; Pedestrian; Monte Carlo method; State (computer science); Stylized fact; Track (disk drive); Estimation; Pedestrian detection; Algorithm; Simulation; Statistics; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004738634,0.00005633681,0.0001348268,0.00002722856,0.00003434106,0.000008285829,0.00007590008,0.00002580242,8.87512e-7],"category_scores_gemma":[0.00002806667,0.00003863523,0.00004114304,0.0001199269,0.00003485355,0.0002543459,5.196817e-7,0.00008364569,1.053378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003874481,"about_ca_system_score_gemma":0.00002000036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001318359,"about_ca_topic_score_gemma":0.0001311802,"domain_scores_codex":[0.9992116,0.00001759082,0.00050815,0.0000436927,0.0001519318,0.00006699922],"domain_scores_gemma":[0.9991387,0.0001168024,0.000292995,0.00008792651,0.000348127,0.00001541641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00007279462,0.00001914646,0.06505668,0.0001068727,0.00001245599,2.015884e-7,0.002658158,0.9211594,0.007784619,0.0001164872,6.396913e-7,0.003012527],"study_design_scores_gemma":[0.0006495288,0.00007204396,0.7746394,0.0001521317,0.00004286977,0.000002940961,0.002737341,0.2123844,0.008238105,0.001003183,0.00001307387,0.00006498234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8015453,0.00002736841,0.1980428,0.00001301018,0.0001795807,0.0001695239,0.00000822082,0.000006301259,0.000007905128],"genre_scores_gemma":[0.9963973,0.00003710617,0.003500944,0.000001336147,0.00004309611,0.000007039099,0.000003955844,0.000008110203,0.000001130621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7095827,"threshold_uncertainty_score":0.1575499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01373071833749349,"score_gpt":0.285424270835138,"score_spread":0.2716935524976445,"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."}}