{"id":"W4386156064","doi":"10.32920/24034107","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; Data collection; Perspective (graphical); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068285,0.0001554097,0.0002852754,0.0003518024,0.000337311,0.0002758324,0.0001861901,0.0002392147,0.0001678584],"category_scores_gemma":[0.0002425714,0.0001712437,0.0001063913,0.0002874475,0.0002464673,0.0001272675,0.0001947344,0.0005044818,0.00002695561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007381153,"about_ca_system_score_gemma":0.0005354811,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3765333,"about_ca_topic_score_gemma":0.9185613,"domain_scores_codex":[0.9983592,0.0003166388,0.0003593736,0.000449704,0.0002689759,0.0002460807],"domain_scores_gemma":[0.9989855,0.0003862132,0.000110205,0.0003032316,0.0001140049,0.0001008929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001008766,0.0006384859,0.08108685,0.001533516,0.0008225988,0.0001313867,0.2677809,0.1606644,0.00000450824,0.3184039,0.009580352,0.1592522],"study_design_scores_gemma":[0.0001187524,0.000007913977,0.0007795562,0.0001864065,0.00006334175,3.298329e-7,0.07992304,0.7717398,0.000001141927,0.144574,0.002260584,0.0003451659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7316239,0.0002345632,0.08270132,0.0200933,0.002874,0.001489109,0.0001406084,0.0009849627,0.1598583],"genre_scores_gemma":[0.9850491,0.0004676729,0.0003279654,0.0001227393,0.0001994789,0.00001815664,0.0001202405,0.00001584213,0.0136788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6110753,"threshold_uncertainty_score":0.6983115,"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."}}