{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01121267,0.0002116354,0.000595959,0.001041906,0.0008552659,0.000144226,0.002276145,0.0002066437,0.001354128],"category_scores_gemma":[0.0001993161,0.0001587221,0.0003739775,0.003283728,0.001299791,0.001056233,0.000009241813,0.00176324,0.00002896862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002233016,"about_ca_system_score_gemma":0.002020074,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.100106,"about_ca_topic_score_gemma":0.5800961,"domain_scores_codex":[0.9900923,0.002307069,0.001359915,0.000536121,0.004889455,0.0008151747],"domain_scores_gemma":[0.9919657,0.001289739,0.0005339592,0.001105415,0.004703294,0.000401877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001274801,0.0004383086,0.9717141,0.0002439417,0.0003216312,0.00004219354,0.0118655,0.001629489,0.001624708,0.002317152,0.0009693949,0.007558823],"study_design_scores_gemma":[0.001511413,0.000754112,0.9587517,0.0004160132,0.0003182861,1.886587e-7,0.01581983,0.0001501348,0.0007693284,0.0004126432,0.02085296,0.0002433668],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992242,0.0001521253,0.002423355,0.003156657,0.0002060613,0.001225996,0.0001451873,0.00001983908,0.0004288164],"genre_scores_gemma":[0.9969102,0.0005696983,0.0008445951,0.00002128188,0.0001348894,0.00003792373,0.00006329732,0.00003761047,0.00138051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4799901,"threshold_uncertainty_score":0.9995587,"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."}}