{"id":"W4386158158","doi":"10.32920/24034107.v1","title":"Pedestrian Dynamics in Smart Cities: Ubiquitous Sensing, Interactions, and Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pedestrian; Computer science; Context (archaeology); Data science; Perspective (graphical); Data collection; Distraction; Human–computer interaction; Transport engineering; Engineering; Artificial intelligence; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005267356,0.0004882195,0.0004980526,0.0006648809,0.0004197071,0.00156969,0.0007645105,0.0008845823,0.001664539],"category_scores_gemma":[0.001838562,0.0004749849,0.0007472859,0.0008479439,0.0006899876,0.002001104,0.001151108,0.0008967735,0.0003714625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008326509,"about_ca_system_score_gemma":0.0005963453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01776644,"about_ca_topic_score_gemma":0.01200965,"domain_scores_codex":[0.9997086,0.00008943672,0.00001203528,0.00009171845,0.00005841567,0.00003976053],"domain_scores_gemma":[0.9995597,0.0002323753,0.00007419712,0.00004069732,0.00005767362,0.00003534277],"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":[0.00009250281,0.000135735,0.03008677,0.0002978541,0.0001064628,0.0003042881,0.001589209,0.7961674,0.001573435,0.09315944,0.005877437,0.0706095],"study_design_scores_gemma":[0.000003420145,0.00003127305,0.00454459,0.00004766779,0.0000161687,0.00005639083,0.0002847499,0.9695062,0.0001351557,0.02213597,0.003219407,0.00001897533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3962626,0.007107006,0.5609865,0.004548004,0.0004211167,0.000160852,0.001518571,0.0006607032,0.02833469],"genre_scores_gemma":[0.9537902,0.004957784,0.03278394,0.0002005927,0.000163812,0.0001489007,0.0004497793,0.00005746221,0.007447517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01776644,"threshold_uncertainty_score":0.03532606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06750852456198617,"score_gpt":0.3412313904296121,"score_spread":0.273722865867626,"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."}}