{"id":"W3058076446","doi":"10.1016/j.tra.2020.07.018","title":"Analyzing the structural properties of bike-sharing networks: Evidence from the United States, Canada, and China","year":2020,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bike sharing; Cluster analysis; Spatial analysis; Computer science; Scale (ratio); China; Transport engineering; Clustering coefficient; Spatial ecology; Geography; Cartography; Engineering; Artificial intelligence","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":[0.001598715,0.0003943972,0.0006425434,0.003303656,0.003540883,0.001918878,0.001760104,0.00066616,0.003170352],"category_scores_gemma":[0.01036834,0.0002989444,0.0005178963,0.01257084,0.002053642,0.001506671,0.001467301,0.0009096712,0.0002765782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01560374,"about_ca_system_score_gemma":0.02264791,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9829511,"about_ca_topic_score_gemma":0.9903776,"domain_scores_codex":[0.998575,0.0002490778,0.00007275026,0.0002619413,0.0003671056,0.0004740955],"domain_scores_gemma":[0.9891383,0.002123093,0.002201525,0.0006673235,0.004510138,0.001359568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001123961,0.00007931189,0.9814348,0.00006888746,0.0002342529,0.0001191791,0.003165608,0.001501655,0.0001330688,0.003042768,0.00156184,0.008546357],"study_design_scores_gemma":[0.00001745544,0.00002085202,0.9847456,0.0000645472,0.0001306997,0.00003305425,0.009007266,0.002794538,0.0001194907,0.0009375408,0.00210663,0.00002227526],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944592,0.0004746656,0.0002119458,0.0004331822,0.000006659169,0.00001875614,0.001421006,0.000005411507,0.002969138],"genre_scores_gemma":[0.9977207,0.0003911169,0.0001176277,0.00004552714,0.000003536127,0.000009579143,0.001191119,0.000002616091,0.0005180738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0170489,"threshold_uncertainty_score":0.1132135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1843885656479604,"score_gpt":0.4151436299168606,"score_spread":0.2307550642689002,"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."}}