{"id":"W1484481195","doi":"10.1093/jeg/4.2.131","title":"Spatial evolution of the US urban system","year":2004,"lang":"en","type":"preprint","venue":"Journal of Economic Geography","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; University of Toronto; John D. and Catherine T. MacArthur Foundation; National Science Foundation","keywords":"Econometrics; Contrast (vision); Census; Distribution (mathematics); Population; Range (aeronautics); Market size; Economics; Population growth; Parametric statistics; Spatial dependence; Geography; Conditional probability distribution; Spatial distribution; Statistics; Mathematics; Demography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009967078,0.0003333468,0.00149293,0.001053443,0.0001047064,0.0001028312,0.001151368,0.0003593898,0.0001083533],"category_scores_gemma":[0.00003027141,0.0003021893,0.002609304,0.0001493848,0.0001826479,0.0001570382,0.0003528348,0.0006454676,0.00005381726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173351,"about_ca_system_score_gemma":0.0003842665,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009965664,"about_ca_topic_score_gemma":0.0004702405,"domain_scores_codex":[0.9963725,0.00003973832,0.002815367,0.0004246476,0.00006946474,0.0002783079],"domain_scores_gemma":[0.9919361,0.00003980615,0.007046416,0.0007190909,0.0001270272,0.0001315379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006063653,0.0001128972,0.6118299,0.0002428527,0.001539802,0.000004153008,0.000108408,0.1463952,0.000004180938,0.2390573,0.0005277282,0.0001169778],"study_design_scores_gemma":[0.002327011,0.0003048127,0.6635962,0.0008688217,0.0004940777,0.0000914794,0.0001773952,0.02335922,0.00008541457,0.2967276,0.01086939,0.001098598],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726517,0.007064285,0.005422832,0.0009784553,0.005832057,0.00030884,0.0004902193,0.00001121406,0.007240355],"genre_scores_gemma":[0.9983551,0.0004015334,0.0002296804,0.00003982711,0.0008763152,0.000007447563,0.000007859605,0.0000337182,0.00004854889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.123036,"threshold_uncertainty_score":0.999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186776870702957,"score_gpt":0.182819339992914,"score_spread":0.1709515712858844,"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."}}