{"id":"W2942082019","doi":"10.1177/0361198119834917","title":"Analyzing Transit User Behavior with 51 Weeks of Smart Card Data","year":2019,"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":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Intrapersonal communication; Context (archaeology); Public transport; Product (mathematics); Transit (satellite); Typology; Ticket; Smart card; Advertising; Computer science; Business; Interpersonal communication; Psychology; Applied psychology; Geography; Transport engineering; Engineering; Mathematics; Communication; Computer security","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":[0.0005229877,0.0003077657,0.000278136,0.002297645,0.0003842626,0.0006040605,0.0003893172,0.000309667,0.0009063131],"category_scores_gemma":[0.002424641,0.0001717727,0.0003747306,0.003883414,0.0002201212,0.0003102423,0.0004398138,0.0002315243,0.0003784923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001620256,"about_ca_system_score_gemma":0.00136947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3968008,"about_ca_topic_score_gemma":0.4920232,"domain_scores_codex":[0.9995055,0.00008996418,0.00003869499,0.0001154384,0.0001430149,0.0001074264],"domain_scores_gemma":[0.9984931,0.0004104504,0.0002613524,0.0002426436,0.0004697421,0.0001226851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001614135,0.0001214338,0.9613094,0.00006017911,0.0001196819,0.0001970935,0.001187753,0.004510527,0.002473463,0.0001929011,0.00235685,0.02730926],"study_design_scores_gemma":[0.000004629034,0.00005870128,0.982254,0.00001204081,0.00002290757,0.0000710945,0.001046346,0.01181434,0.0008337063,0.00005451633,0.003810937,0.00001682696],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877823,0.000042378,0.001319121,0.00003826369,0.00000562469,0.00006256096,0.01011303,0.00004818989,0.0005885081],"genre_scores_gemma":[0.9789474,0.00006324936,0.003921469,0.00001349424,0.000008390193,0.00008418587,0.01589974,0.00001299389,0.001049081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3968008,"threshold_uncertainty_score":0.7889823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1163565779043884,"score_gpt":0.4169928179727246,"score_spread":0.3006362400683362,"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."}}