{"id":"W2897608845","doi":"10.1155/2018/2696037","title":"Transportation Planning through GIS and Multicriteria Analysis: Case Study of Beijing and XiongAn","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Beijing; Transport engineering; Geographic information system; Transportation planning; Analytic hierarchy process; Flow network; Urbanization; Population; Computer science; Operations research; Engineering; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009844693,0.0008029462,0.0004409939,0.002050297,0.001546155,0.001310223,0.001303513,0.001153367,0.002709978],"category_scores_gemma":[0.00190978,0.0004578463,0.0007122193,0.004967051,0.001157132,0.001266996,0.0009874734,0.0006870797,0.0001751422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006820175,"about_ca_system_score_gemma":0.002346626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1706674,"about_ca_topic_score_gemma":0.2038883,"domain_scores_codex":[0.9992563,0.0004333358,0.00003422586,0.0000694912,0.0001083401,0.00009837041],"domain_scores_gemma":[0.9988258,0.0007263005,0.0001086573,0.00007174921,0.0001572747,0.0001101175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005829831,0.002001973,0.162671,0.0006241102,0.0003739044,0.02955809,0.00972752,0.6812908,0.003675473,0.0172625,0.004885447,0.08734608],"study_design_scores_gemma":[0.0001005835,0.0004930706,0.08833753,0.00008137826,0.0001625311,0.0008911282,0.02590308,0.8698336,0.002069161,0.003822505,0.00819791,0.0001075565],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917087,0.0001206354,0.002861333,0.0003015866,0.000006162337,0.0001120254,0.0002497891,0.00004453072,0.004595255],"genre_scores_gemma":[0.9912038,0.0002500821,0.006398526,0.00001565275,0.000003969294,0.00008194961,0.0002277905,0.00001194477,0.001806164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1706674,"threshold_uncertainty_score":0.339348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0296698602887331,"score_gpt":0.362906800419304,"score_spread":0.3332369401305709,"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."}}