{"id":"W2783972495","doi":"10.1139/cjce-2017-0559","title":"LRT passengers’ responses to advanced passenger information system (APIS) in case of information inconsistency and train crowding","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Public transport; Transport engineering; Train; Transit (satellite); Multinomial logistic regression; Travel behavior; Service (business); Crowding; Passenger information; Geography; Computer science; Business; Engineering; Marketing; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001652743,0.0001475659,0.0002244448,0.0006450947,0.0004427312,0.0008243038,0.0003069499,0.0004176224,0.001311343],"category_scores_gemma":[0.009509991,0.0001515581,0.0003172331,0.0006131659,0.000413218,0.0004777799,0.0008276177,0.0005713966,0.0001288897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008783655,"about_ca_system_score_gemma":0.0004606687,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03658279,"about_ca_topic_score_gemma":0.05019392,"domain_scores_codex":[0.9987179,0.0005906711,0.00009070243,0.0001198912,0.0002934267,0.0001873944],"domain_scores_gemma":[0.9937499,0.002281509,0.002514671,0.0002817081,0.0008587766,0.0003135771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003786242,0.0001442137,0.9638051,0.00007290809,0.00008005586,0.0003682727,0.02150993,0.0008527365,0.001944014,0.00008840964,0.0002058848,0.01054984],"study_design_scores_gemma":[0.000005256014,0.0002668767,0.9662042,0.00001557472,0.0000312482,0.00009790711,0.03121105,0.001248152,0.0004068002,0.00005225857,0.0004421168,0.00001855057],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996278,0.000008146309,0.00005372628,0.00002241324,8.55747e-7,0.000006057098,0.00002192053,0.000001203406,0.0002579135],"genre_scores_gemma":[0.9996527,0.00001442136,0.00009785969,0.00001456212,0.000001332291,0.000007371773,0.00004202676,4.977192e-7,0.0001692584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9634172,"threshold_uncertainty_score":0.07273972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008050662225839797,"score_gpt":0.2257638125151636,"score_spread":0.2177131502893238,"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."}}