{"id":"W4237636990","doi":"10.1002/cav.243","title":"A social agent pedestrian model","year":2008,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; University College London; Kwantlen Polytechnic University; University of Surrey; Loyola University Chicago; Sogang University; University of Chicago","keywords":"Pedestrian; Computer science; Set (abstract data type); Fear of crime; Data science; Criminology; Transport engineering; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003904375,0.00008436246,0.00008573756,0.00005892017,0.0001345562,0.00003213229,0.0000461064,0.0000396339,0.00002327134],"category_scores_gemma":[0.000001395601,0.0000896155,0.00002918482,0.0000891594,0.00002434637,0.0001075152,0.00001971174,0.00006928936,0.0000340931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002353782,"about_ca_system_score_gemma":0.000009799199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.020088e-7,"about_ca_topic_score_gemma":0.000004368543,"domain_scores_codex":[0.9995838,0.000009623311,0.0001297632,0.00008687954,0.00009323294,0.00009671314],"domain_scores_gemma":[0.9998555,0.000009624139,0.00001391929,0.00005028969,0.00002049111,0.00005015212],"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":[0.00003196557,0.0001240299,0.001015598,0.0001049459,0.00009003566,0.00001614385,0.01399526,0.6750605,0.001139433,0.0998193,0.08768784,0.120915],"study_design_scores_gemma":[0.0002690696,0.00001973064,0.005154087,0.000003457945,0.000003716723,0.000007726687,0.00001717723,0.9924934,0.00001590565,0.0001885487,0.00172148,0.0001057198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3977797,0.0000334257,0.5981496,0.0001375688,0.0001141135,0.00006280464,0.000003459755,0.0002625822,0.003456744],"genre_scores_gemma":[0.9966503,0.00004539789,0.00239236,0.0002224508,0.0001354884,0.000004119528,0.00001785974,0.00001228519,0.0005197201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5988706,"threshold_uncertainty_score":0.3654414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03113107683181171,"score_gpt":0.2490520596388837,"score_spread":0.217920982807072,"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."}}