{"id":"W4293711790","doi":"10.1155/2022/8062932","title":"Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jeju National University","keywords":"Cluster analysis; Dimension (graph theory); Demand forecasting; Computer science; Data mining; Artificial intelligence; Statistics; Pattern recognition (psychology); Mathematics; Operations research","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005933993,0.0001184577,0.0002387069,0.0006047657,0.0001011685,0.00001547384,0.000101254,0.00004159909,0.00002361646],"category_scores_gemma":[0.0000211109,0.0001307044,0.00007166863,0.0005847891,0.000007766283,0.0004510662,0.000004032828,0.0004461569,6.553593e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004898487,"about_ca_system_score_gemma":0.00005142045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002216921,"about_ca_topic_score_gemma":0.00006141199,"domain_scores_codex":[0.9983121,0.00008740243,0.0007057177,0.0001200984,0.0005520344,0.0002226138],"domain_scores_gemma":[0.9994793,0.00004922225,0.000230773,0.00009562771,0.00008759862,0.00005748861],"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":[0.0001445652,0.00001981099,0.01297828,0.00007164291,0.00001891686,0.00009022968,0.0008885829,0.8532041,0.1312696,0.000003193659,0.00002821707,0.001282864],"study_design_scores_gemma":[0.003637511,0.0004902551,0.3762116,0.0004695604,0.00005754443,0.0002468108,0.0009688666,0.5993224,0.01709134,0.0001289074,0.001034296,0.0003409516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787626,0.0005060199,0.01958548,0.00002333233,0.0008430342,0.0002057979,0.000006544939,0.0000464632,0.00002067952],"genre_scores_gemma":[0.9969842,0.00003874976,0.002784539,0.00000475322,0.0001120119,0.00001152295,0.0000157788,0.00003551126,0.00001289103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3632333,"threshold_uncertainty_score":0.5329973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147346467722372,"score_gpt":0.2573836880493913,"score_spread":0.2426490412771541,"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."}}