{"id":"W4399147174","doi":"10.1109/trustcom60117.2023.00324","title":"A Big Data Science and Engineering Solution for Transit Performance Analytics","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Public transport; Big data; Destinations; Transit (satellite); Analytics; Transport engineering; Computer science; Usability; Work (physics); Appeal; Data analysis; Data science; Engineering; Tourism; Geography; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.002313375,0.00003557874,0.00005626528,0.0001483568,0.0006858908,0.00007917008,0.0002591698,0.00002237592,0.00001405226],"category_scores_gemma":[0.0004263873,0.00003452045,0.00001270042,0.001193544,0.0002433287,0.0003239143,0.00003305457,0.00002898093,0.000008784066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004540363,"about_ca_system_score_gemma":0.0002563777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008932812,"about_ca_topic_score_gemma":0.004602556,"domain_scores_codex":[0.9992689,0.000008789083,0.00008298887,0.0001966455,0.0002499361,0.0001926887],"domain_scores_gemma":[0.9995168,0.00009288754,0.00001314969,0.0001944631,0.0001194773,0.00006315699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002200617,0.00009559887,0.01031294,0.0003408484,0.00008179792,0.000001003154,0.02413425,0.01411685,0.00701927,0.03440604,0.00365441,0.905815],"study_design_scores_gemma":[0.00006341017,0.00001033472,0.005595264,0.000005965074,0.00002034898,4.17431e-8,0.00077594,0.9765384,0.00007421145,0.00005361329,0.01679912,0.00006335891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7453625,0.00005668979,0.2428036,0.006734041,0.000325718,0.0005017061,0.00003776529,0.0004034631,0.003774615],"genre_scores_gemma":[0.9989058,0.00007921409,0.0003850416,0.00004047018,0.0001109928,0.000006860425,0.00001635355,0.000002302184,0.0004529946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9624215,"threshold_uncertainty_score":0.5275387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230710030310321,"score_gpt":0.3265146481812199,"score_spread":0.2034436451501878,"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."}}