{"id":"W4281608338","doi":"10.24018/ejai.2022.1.3.9","title":"Machine Learning Model to Forecast Demand of Boston Bike-Ride Sharing","year":2022,"lang":"en","type":"article","venue":"European Journal of Artificial Intelligence and Machine Learning","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Renting; Bike sharing; Revenue; Economic rent; Transport engineering; Demand forecasting; Operations research; Computer science; Revenue sharing; Engineering; Business; Finance; Economics; Civil engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007862205,0.0008678756,0.0006517174,0.0009020014,0.000295261,0.0007914112,0.001151189,0.001147514,0.001959451],"category_scores_gemma":[0.002539137,0.0003069341,0.000628692,0.0009576,0.0002522315,0.0007462821,0.0003639962,0.001089111,0.0005647116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001669001,"about_ca_system_score_gemma":0.0006212892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07375666,"about_ca_topic_score_gemma":0.04029342,"domain_scores_codex":[0.9997159,0.00006089497,0.00002511374,0.0001099027,0.00003760729,0.00005049931],"domain_scores_gemma":[0.9986759,0.0007728336,0.0001179198,0.00005368956,0.0003312581,0.00004844613],"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":[0.00008167083,0.00009053932,0.009601908,0.0000253753,0.00002246528,0.00005595521,0.00001973774,0.9792467,0.0003187844,0.0002708979,0.001541641,0.008724271],"study_design_scores_gemma":[0.000002065006,0.00000613778,0.0006521524,0.000001005408,0.000001391419,0.000001900462,0.000003753509,0.9991484,0.00004151357,0.00007419228,0.00006573071,0.000001862026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9298486,0.0006950425,0.05833027,0.001345583,0.0001238629,0.00007400993,0.00522754,0.0008410704,0.003514203],"genre_scores_gemma":[0.9847741,0.0001542905,0.007867488,0.00008823173,0.00003487591,0.00006300101,0.004515343,0.00001846804,0.002484199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07375666,"threshold_uncertainty_score":0.1466547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08167029365445776,"score_gpt":0.3143655153543865,"score_spread":0.2326952216999287,"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."}}