{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004023893,0.000129521,0.0002335932,0.00009293526,0.0002304511,0.0003864356,0.0001367411,0.00008024225,0.00002504423],"category_scores_gemma":[0.00003579023,0.0001364152,0.00007986708,0.0003141109,0.00001444264,0.001214557,0.00002352541,0.00007331654,0.0000266242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008899198,"about_ca_system_score_gemma":0.000113295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000313971,"about_ca_topic_score_gemma":0.006783491,"domain_scores_codex":[0.9987422,0.0001139829,0.00023172,0.000478616,0.000220507,0.000212925],"domain_scores_gemma":[0.9988385,0.0001693503,0.0001235039,0.0002840256,0.0004804724,0.0001041658],"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":[0.00003690795,0.0006856978,0.00919131,0.00171925,0.0002453033,0.00004665175,0.004033689,0.000001639059,0.05513342,0.031698,0.002288444,0.8949197],"study_design_scores_gemma":[0.01790836,0.001594572,0.6205131,0.001499103,0.0004172878,0.0015609,0.01048509,0.1188822,0.1590814,0.02383485,0.0388111,0.005411927],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4813279,0.00002211217,0.5161192,0.0007221657,0.0002332273,0.0003600265,0.00005289287,0.0002639684,0.0008984858],"genre_scores_gemma":[0.9970964,0.00000220926,0.002215681,0.00009849072,0.00005361194,0.000212607,0.00008049536,0.00001075483,0.0002297106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8895078,"threshold_uncertainty_score":0.5562852,"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."}}