{"id":"W2144165524","doi":"10.3141/2274-05","title":"Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Community College; University of British Columbia","funders":"","keywords":"Incidence (geometry); Reliability (semiconductor); Schedule; Headway; Service (business); Computer science; Transit (satellite); Public transport; Transport engineering; Operations research; Simulation; Engineering; Mathematics; Business","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":[0.001391968,0.0004596425,0.0004254172,0.003052044,0.0002192787,0.0008634026,0.0005359946,0.0002930273,0.0009956093],"category_scores_gemma":[0.00897114,0.0002842558,0.000547893,0.00486454,0.0002421245,0.0008891968,0.0006312588,0.0003392425,0.0003410031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387319,"about_ca_system_score_gemma":0.0008773742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06315853,"about_ca_topic_score_gemma":0.05811828,"domain_scores_codex":[0.9986621,0.0004139469,0.0001197633,0.0003652825,0.0003420321,0.00009683231],"domain_scores_gemma":[0.9949552,0.001988827,0.001383337,0.0007473953,0.0007240796,0.0002011095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002475056,0.0002434885,0.8492635,0.00007180285,0.0001463362,0.00008290523,0.0004926335,0.07931931,0.002359946,0.0008132574,0.0006099123,0.06634935],"study_design_scores_gemma":[0.0000260823,0.0002503711,0.5280823,0.00001226825,0.00006667415,0.00006955631,0.0006643474,0.4668615,0.001776659,0.0007133197,0.001441523,0.00003535429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704245,0.00002468196,0.02597291,0.00003071198,0.000006956252,0.00009624978,0.002207395,0.0001707366,0.001065841],"genre_scores_gemma":[0.975511,0.00004084537,0.01864284,0.000006446819,0.000008699011,0.00006328143,0.005093295,0.00001920089,0.00061437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06315853,"threshold_uncertainty_score":0.1255818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1618059882511467,"score_gpt":0.4344851901431084,"score_spread":0.2726792018919617,"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."}}