{"id":"W2948674847","doi":"10.1155/2019/6873912","title":"Measuring Retiming Responses of Passengers to a Prepeak Discount Fare by Tracing Smart Card Data: A Practical Experiment in the Beijing Subway","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Retiming; Beijing; Elasticity (physics); Tracing; Computer science; Econometrics; Economics; China; Parallel computing; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001795434,0.00009565081,0.0002213147,0.0001409994,0.0001145305,0.0000387699,0.0002183717,0.00005455463,0.00001078317],"category_scores_gemma":[0.0002517182,0.00007945959,0.00005774578,0.0003320378,0.00003459799,0.001122336,0.000001644424,0.0002014079,6.375165e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008304333,"about_ca_system_score_gemma":0.0001905111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001634354,"about_ca_topic_score_gemma":0.001133053,"domain_scores_codex":[0.9980236,0.0002151049,0.0006538271,0.0001791899,0.0007510985,0.0001772105],"domain_scores_gemma":[0.9987401,0.0003685167,0.0004633526,0.0001699949,0.0001929248,0.00006508121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.002796747,0.0003198092,0.120922,0.0001531797,0.0000832475,0.00007256655,0.468224,0.3666191,0.03555918,0.0009036744,0.0001894639,0.004157067],"study_design_scores_gemma":[0.0026959,0.0007233138,0.5800289,0.001820044,0.0002251553,0.00001082688,0.3975302,0.0003354585,0.007374355,0.00008389062,0.008645802,0.0005261325],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886657,0.0001883915,0.008933133,0.001484266,0.000230725,0.0003217671,0.00003259133,0.000009882113,0.000133551],"genre_scores_gemma":[0.9904426,0.00009702586,0.009276399,0.0000503823,0.00004074103,0.000006373632,0.00005237894,0.00001032612,0.0000238052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4591069,"threshold_uncertainty_score":0.3240268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04992380711718025,"score_gpt":0.3503088364430138,"score_spread":0.3003850293258336,"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."}}