{"id":"W2466616840","doi":"10.5539/mas.v10n8p32","title":"Urban Growth Structure and Travel Behavior in Tehran City","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Urban sprawl; Smart growth; Traffic congestion; Transport engineering; Growth management; Business; Land use; Population growth; Urban planning; Environmental planning; Dependency (UML); Population; Geography; Computer science; Environmental health; Civil engineering; Engineering","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":[0.0006833005,0.0001084717,0.0001403982,0.0001061498,0.0004804897,0.00009400288,0.0005180971,0.00007845672,0.00008033066],"category_scores_gemma":[0.0000381864,0.00007547312,0.00002024905,0.0005178824,0.002008858,0.0004391139,0.00004278454,0.0001032116,0.000003542594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001023269,"about_ca_system_score_gemma":0.0002106197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006034459,"about_ca_topic_score_gemma":0.003211277,"domain_scores_codex":[0.9983077,0.00001824227,0.0001674214,0.0005276501,0.0005280765,0.0004509266],"domain_scores_gemma":[0.9994925,0.00003773768,0.00004750132,0.0001892694,0.00004595423,0.0001870814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001158272,0.00003797334,0.7766314,0.000003385043,5.212938e-7,0.00000212398,0.005070211,4.669654e-8,0.2021087,0.007263809,0.0000135528,0.008856716],"study_design_scores_gemma":[0.0002949684,0.000007586744,0.973884,0.000006648876,0.000004733014,1.622703e-7,0.0002087099,0.00003281781,0.006498863,0.01885424,0.00005267188,0.0001545536],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990213,0.00002523788,0.00245176,0.000297837,0.00007345744,0.0002869818,0.00001628242,0.00003971678,0.006595713],"genre_scores_gemma":[0.9994093,0.000006486578,0.0002343352,0.00006698405,0.00005212069,0.00001704208,6.0458e-7,0.000005111173,0.0002080576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1972526,"threshold_uncertainty_score":0.740172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924621277161456,"score_gpt":0.2731791505066024,"score_spread":0.2539329377349879,"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."}}