{"id":"W3217357390","doi":"10.1109/5gwf52925.2021.00039","title":"Human Activity Recognition and People Count for a SMART Public Transportation System","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Activity recognition; Computer science; Public transport; Artificial intelligence; Machine learning; Human–computer interaction; Engineering; Transport engineering","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":[0.0004200555,0.0007476584,0.0006885067,0.0008472783,0.0002367539,0.0004214259,0.0004763133,0.0006709954,0.001628671],"category_scores_gemma":[0.000811265,0.0001294015,0.0003496674,0.0006918194,0.0001474252,0.0004363774,0.0003609761,0.0005766788,0.001083191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004711048,"about_ca_system_score_gemma":0.0004237872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01108954,"about_ca_topic_score_gemma":0.01514081,"domain_scores_codex":[0.9996461,0.00005710746,0.00002194105,0.0001328457,0.0000829637,0.00005915379],"domain_scores_gemma":[0.9997892,0.00004105936,0.00003285178,0.00003424382,0.00006538891,0.00003722303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001590578,0.001884898,0.08413957,0.0003273335,0.000329485,0.0008961904,0.000176519,0.1776126,0.05200214,0.00096922,0.02317678,0.6568947],"study_design_scores_gemma":[0.00002618844,0.0002566525,0.06576803,0.00001329779,0.00003858766,0.0001661014,0.0001567955,0.9178376,0.01273452,0.0006653522,0.0023104,0.00002644743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.92374,0.0005184981,0.06199054,0.0004186786,0.0001533907,0.0001265133,0.005905632,0.004743271,0.00240339],"genre_scores_gemma":[0.9545616,0.0001362634,0.03173341,0.00006084464,0.00005162424,0.00008765848,0.01090566,0.00003832694,0.002424589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01108954,"threshold_uncertainty_score":0.02204996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06827436082284524,"score_gpt":0.2636426873568802,"score_spread":0.1953683265340349,"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."}}