{"id":"W2940976698","doi":"10.1007/978-3-030-17795-9_6","title":"Transfer Probability Prediction for Traffic Flow with Bike Sharing Data: A Deep Learning Approach","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bike sharing; Computer science; Support vector machine; Heuristic; Restricted Boltzmann machine; Deep learning; Transfer of learning; Artificial intelligence; Machine learning; Transfer (computing); Algorithm; Data mining; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004871222,0.0003717724,0.0004923766,0.0002012864,0.00009685667,0.00009431476,0.0003037765,0.0001979552,0.000001848349],"category_scores_gemma":[0.000006268377,0.0003447582,0.00005538694,0.00005412311,0.00004463756,0.0003977265,0.00008849827,0.0004620896,0.000001684361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174718,"about_ca_system_score_gemma":0.00001072755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003677079,"about_ca_topic_score_gemma":0.0000244985,"domain_scores_codex":[0.9982012,0.00001409057,0.0005883378,0.0007167159,0.0001969342,0.0002827162],"domain_scores_gemma":[0.9993469,0.00007400638,0.00007562726,0.00041002,0.00004173239,0.00005178693],"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":[0.00001862609,0.00001217473,0.00005908643,0.002533082,0.0000718158,0.000001085965,0.000221495,0.8240833,8.834534e-7,0.01110811,0.00003791539,0.1618524],"study_design_scores_gemma":[0.0002152711,0.0001051759,0.000004924181,0.001201233,0.00004904964,0.00001271075,0.0001563188,0.8922612,0.000002499495,0.00007454499,0.105619,0.0002981145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003976048,0.008757297,0.9584241,0.000002184653,0.0007069984,0.001915584,0.00003940322,0.001841846,0.02791497],"genre_scores_gemma":[0.9767783,0.006402333,0.01100767,0.00001179838,0.0004850492,0.0001603029,0.00064678,0.0002090718,0.004298685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9763807,"threshold_uncertainty_score":0.9999005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02391686196351556,"score_gpt":0.2293877714873913,"score_spread":0.2054709095238758,"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."}}