{"id":"W4210754377","doi":"10.3390/s22031060","title":"Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Canada Research Chairs","keywords":"Autoregressive integrated moving average; Bike sharing; Computer science; Benchmark (surveying); Demand forecasting; Deep learning; TRIPS architecture; Demand patterns; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Set (abstract data type); Machine learning; Operations research; Time series; Demand management; Transport engineering; Engineering; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003047164,0.001027484,0.0004405162,0.0005203615,0.0003061777,0.0005317276,0.0009541379,0.0004960707,0.001518158],"category_scores_gemma":[0.0007823514,0.0002489879,0.0004101477,0.0006449737,0.0002187183,0.0007863708,0.0007778273,0.0008428022,0.000349355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001645062,"about_ca_system_score_gemma":0.0014655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2396925,"about_ca_topic_score_gemma":0.2419178,"domain_scores_codex":[0.9998494,0.00001652344,0.000006125771,0.00004293758,0.0000228578,0.0000620624],"domain_scores_gemma":[0.9997786,0.0000388846,0.00002313034,0.0000183119,0.0001016781,0.00003947928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004432559,0.0004957905,0.06460268,0.000085625,0.0001339106,0.0002348927,0.00008555678,0.86495,0.002599578,0.0007745013,0.005905619,0.05968853],"study_design_scores_gemma":[0.000004710514,0.00002117867,0.003468954,0.000002708769,0.000005153382,0.000004107604,0.00003375372,0.995753,0.000368836,0.0001655544,0.00016767,0.000004285631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570468,0.0002866561,0.03387833,0.0005579729,0.00007313144,0.00004972494,0.003353179,0.001087323,0.003666788],"genre_scores_gemma":[0.9904165,0.00005872417,0.004645429,0.00003750624,0.00001064955,0.00002175253,0.003354353,0.00001328201,0.001441863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2396925,"threshold_uncertainty_score":0.4765946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1540627920112799,"score_gpt":0.3563155326095609,"score_spread":0.202252740598281,"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."}}