{"id":"W4400510070","doi":"10.1007/s44212-024-00053-9","title":"Understanding pedestrian movement using urban sensing technologies: the promise of audio-based sensors","year":2024,"lang":"en","type":"article","venue":"Urban Informatics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Pedestrian; Movement (music); Computer science; Computer vision; Human–computer interaction; Transport engineering; Engineering; Acoustics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004775645,0.0006555992,0.0003687014,0.001392654,0.0002611206,0.001103588,0.0005071844,0.0005953943,0.001500274],"category_scores_gemma":[0.001617214,0.0001560469,0.0003333198,0.001364794,0.0003375568,0.001169314,0.0007439343,0.0006202958,0.0006424176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002414456,"about_ca_system_score_gemma":0.0002979962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003458529,"about_ca_topic_score_gemma":0.006584776,"domain_scores_codex":[0.999657,0.0001000016,0.00001605682,0.00008777144,0.0001027889,0.00003625693],"domain_scores_gemma":[0.9990079,0.0004417537,0.0001153125,0.0001273174,0.0002500803,0.00005771016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007108155,0.0003969201,0.07159048,0.001503262,0.0002380629,0.0006059355,0.0007877253,0.07852667,0.06670967,0.007212066,0.02059399,0.7511244],"study_design_scores_gemma":[0.00009964358,0.0007628402,0.1492816,0.0008859082,0.0003395358,0.001310376,0.004018761,0.6706625,0.05441953,0.03301543,0.08495638,0.000247488],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3882907,0.007825771,0.5575745,0.003207862,0.001139233,0.0002582659,0.008993309,0.002394923,0.03031542],"genre_scores_gemma":[0.7998051,0.003650205,0.187862,0.0005033641,0.0006455013,0.0001212926,0.004340555,0.0001080561,0.002963984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003458529,"threshold_uncertainty_score":0.006876826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130460549984666,"score_gpt":0.2954515601075431,"score_spread":0.1824055051090765,"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."}}