{"id":"W2371549227","doi":"","title":"The Evolution of Worldwide Metro Systems: A Study on Their Scales and Network Indexes","year":2008,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beijing; Scale (ratio); Population; Metro station; Transport engineering; Geography; Computer science; China; Engineering; Cartography; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004942111,0.00004159623,0.00007479397,0.00003048453,0.0006864931,0.00002089056,0.00006102068,0.00002453515,0.000002189825],"category_scores_gemma":[0.00004041004,0.00002527759,0.00001422433,0.0002476386,0.0001316332,0.00005805789,0.000002710514,0.00003636379,0.000001007928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002724401,"about_ca_system_score_gemma":0.00003766027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00223631,"about_ca_topic_score_gemma":0.00753564,"domain_scores_codex":[0.9993596,0.0001522824,0.0001310156,0.00007839451,0.0001809298,0.00009779512],"domain_scores_gemma":[0.9995079,0.000285931,0.00006257364,0.00006304537,0.00005464724,0.00002588166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002319411,0.00004066299,0.9516376,0.000001840501,0.00001756895,5.005965e-7,0.01034659,0.01457707,0.000001504161,0.02260725,0.0004955961,0.0002506433],"study_design_scores_gemma":[0.000152316,0.00006850291,0.9441789,0.00001642688,0.00000736305,1.603425e-7,0.05407873,0.0004618801,0.000002225185,0.0002008295,0.0007854513,0.00004722364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899148,0.0005946512,0.002501664,0.0001565069,0.0001458695,0.000255297,0.000001749965,0.00004922345,0.006380202],"genre_scores_gemma":[0.9989576,0.00007011697,0.00007382746,0.000009195431,0.00005838031,0.000008755536,0.000001099924,0.000002587176,0.0008183957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04373213,"threshold_uncertainty_score":0.528002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047333045179269,"score_gpt":0.2655176985659166,"score_spread":0.2450443681141239,"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."}}