{"id":"W2989833147","doi":"10.1016/j.jue.2019.103227","title":"Cities in China","year":2019,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Economic Zones and Regional Development","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"China; Economics; Economic geography; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000210214,0.0003275749,0.0003195912,0.002752573,0.001970346,0.001586928,0.0004254577,0.0002789031,0.006814844],"category_scores_gemma":[0.0003782441,0.0002301154,0.0003699108,0.006133788,0.0005715028,0.0007306423,0.0012983,0.0003143837,0.0003844467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005129048,"about_ca_system_score_gemma":0.00886832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2965157,"about_ca_topic_score_gemma":0.4587895,"domain_scores_codex":[0.9995659,0.00003990258,0.00003043809,0.0000911138,0.00007870817,0.000193869],"domain_scores_gemma":[0.9996547,0.00001695271,0.00007708291,0.00001830858,0.0001055669,0.0001273906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001615627,0.0001069198,0.9147108,0.0002340148,0.0001809734,0.0009711722,0.002855286,0.002857888,0.0006419715,0.02219711,0.02193593,0.0331464],"study_design_scores_gemma":[0.00002300801,0.00002476605,0.9749461,0.00002536673,0.00006480081,0.00007264119,0.002676635,0.002003613,0.0001048296,0.001219046,0.01881988,0.00001927458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686633,0.001595008,0.0002509779,0.001899631,0.00006893835,0.00004089252,0.004300225,0.00004834471,0.02313277],"genre_scores_gemma":[0.9915558,0.0004281829,0.0000864837,0.0000747357,0.00002005164,0.0000171958,0.001522026,0.000004245816,0.006291181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2965157,"threshold_uncertainty_score":0.5895796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147194437098443,"score_gpt":0.172700705141773,"score_spread":0.1579812614319287,"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."}}