{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006716381,0.0002641862,0.0001857661,0.003891219,0.000455261,0.001266364,0.0003730594,0.0001992131,0.002408168],"category_scores_gemma":[0.003672619,0.0001389101,0.0003573721,0.006205108,0.0006119959,0.001695192,0.001114783,0.0003359556,0.0002625718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109841,"about_ca_system_score_gemma":0.000440555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01873503,"about_ca_topic_score_gemma":0.02087178,"domain_scores_codex":[0.999478,0.0001026808,0.00003811948,0.0001348818,0.0001359686,0.000110324],"domain_scores_gemma":[0.9981641,0.000313322,0.0006883174,0.0001598887,0.0004452452,0.0002290317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000320032,0.00001832691,0.9709518,0.00003336781,0.00007585578,0.0002084563,0.001540779,0.0021569,0.0005830568,0.00365587,0.0008654249,0.01987812],"study_design_scores_gemma":[0.000001260841,0.00001766964,0.9913357,0.00001074277,0.00001616062,0.00008863219,0.002053781,0.002743161,0.0001433557,0.0003190977,0.003261066,0.000009341444],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892982,0.0003325277,0.001535728,0.0001091304,0.00001077171,0.00001814208,0.0007081703,0.00002900028,0.007958245],"genre_scores_gemma":[0.9980879,0.0001164096,0.0004185612,0.000005344486,0.000007095123,0.00001061713,0.0006829938,0.00001220601,0.0006588149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01873503,"threshold_uncertainty_score":0.03725195,"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."}}