{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001562384,0.0005767439,0.0004973009,0.0005666993,0.0004338018,0.000413962,0.0007096156,0.0007811278,0.0009155257],"category_scores_gemma":[0.003716867,0.0002329841,0.0004490602,0.0008215131,0.0004276049,0.0009010436,0.0005229586,0.0007528329,0.0002056089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008716983,"about_ca_system_score_gemma":0.0005747985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01899371,"about_ca_topic_score_gemma":0.02313854,"domain_scores_codex":[0.9990484,0.0003471363,0.00005646652,0.0002381605,0.0001703989,0.0001393825],"domain_scores_gemma":[0.9969878,0.001433448,0.0002812989,0.0004362979,0.0005448621,0.0003164087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003292594,0.003980812,0.7328112,0.0004054896,0.0004537155,0.002079819,0.004826043,0.1181992,0.046329,0.001512025,0.00243605,0.08367421],"study_design_scores_gemma":[0.0001290702,0.002452076,0.6351325,0.00002508194,0.0001330615,0.0002479417,0.00425775,0.3430992,0.01242796,0.0007009211,0.001282724,0.0001117048],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991176,0.000008800945,0.0005495808,0.00002519846,0.000004540113,0.00001457977,0.0001136953,0.00002160207,0.0001444871],"genre_scores_gemma":[0.9977778,0.00001650825,0.001624081,0.00001682787,0.000005128979,0.00002262451,0.000272024,0.000004864071,0.0002601973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01899371,"threshold_uncertainty_score":0.03776628,"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."}}