{"id":"W2809303669","doi":"10.1145/3219819.3219873","title":"Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bike sharing; Popularity; Computer science; Demand forecasting; Renting; Variance (accounting); Key (lock); Inference; Focus (optics); Operations research; Transport engineering; Artificial intelligence; Engineering; Computer security; Economics","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":[0.001180539,0.00006042493,0.0001104422,0.00005940241,0.0002802795,0.00007218763,0.0001064003,0.00006983558,0.00003955001],"category_scores_gemma":[0.0001368732,0.00005340962,0.00002947609,0.0002177981,0.0000874738,0.0003656093,0.000008370183,0.00004644797,0.000004883855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001513349,"about_ca_system_score_gemma":0.00007705443,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01428764,"about_ca_topic_score_gemma":0.02840571,"domain_scores_codex":[0.9991596,0.00004499435,0.0001951433,0.0002254605,0.0001677513,0.000207066],"domain_scores_gemma":[0.9995834,0.00009131439,0.00005131762,0.00008338912,0.0001416986,0.00004886441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000381265,0.00003043749,0.9830213,0.00006204315,0.00001019912,4.004099e-7,0.009347182,0.00002387688,0.0001551281,0.003783079,0.0002608972,0.003267391],"study_design_scores_gemma":[0.0005183106,0.00007408753,0.9861109,0.00007630803,0.00001253099,5.749222e-8,0.002841454,0.005259145,0.0005328624,0.00300685,0.001460354,0.0001070881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9137923,0.00004456072,0.06854345,0.00008196618,0.0006627366,0.0009641605,0.00003607885,0.00008912037,0.0157856],"genre_scores_gemma":[0.9981319,0.000003197988,0.0004511704,0.00001997729,0.0004388668,0.0001019982,0.00001276361,0.000005335212,0.0008348146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08433955,"threshold_uncertainty_score":0.9922763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04070700800487691,"score_gpt":0.3327050420004349,"score_spread":0.291998033995558,"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."}}