{"id":"W2066722870","doi":"10.3141/2276-06","title":"Detection of Activities of Public Transport Users by Analyzing Smart Card Data","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Smart card; Public transport; Frame (networking); Computer science; Transit (satellite); Travel behavior; Transport engineering; Geography; Data science; Computer security; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004528677,0.0003653851,0.0003003161,0.005375354,0.0001693497,0.0005672281,0.0002949692,0.0003174939,0.001131698],"category_scores_gemma":[0.001789094,0.0001093707,0.0002111593,0.003426686,0.0001476578,0.0004187248,0.0003999633,0.0001926848,0.0006202532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003617866,"about_ca_system_score_gemma":0.0004534175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01136881,"about_ca_topic_score_gemma":0.01226961,"domain_scores_codex":[0.9995078,0.0001121072,0.00005934494,0.0001037045,0.0001631761,0.00005389862],"domain_scores_gemma":[0.9985721,0.0004570498,0.0003143163,0.0001567634,0.0003921621,0.0001076366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003619712,0.0002802288,0.8460115,0.0002327378,0.000065838,0.0003290828,0.0007636861,0.00301995,0.01553148,0.000524497,0.002058603,0.1308204],"study_design_scores_gemma":[0.00001951124,0.0001952078,0.9431391,0.00004468174,0.0000481575,0.0004094538,0.001951304,0.03825927,0.01086715,0.0003464534,0.004681032,0.00003864747],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744757,0.0001513304,0.01201971,0.0000777259,0.00001976616,0.0002414312,0.008824254,0.0003686362,0.003821566],"genre_scores_gemma":[0.9754696,0.0002029545,0.01713168,0.00001790219,0.00001659177,0.0001394592,0.005704348,0.00001350008,0.001303842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01136881,"threshold_uncertainty_score":0.0226053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1786192869811287,"score_gpt":0.4226910986061965,"score_spread":0.2440718116250678,"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."}}