{"id":"W2385617456","doi":"","title":"Economic Development and Regional Balance Research in Yunnan Province Since 1992","year":2014,"lang":"en","type":"article","venue":"Ecological Economy","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Salary; Per capita; Per capita income; Economics; Total personal income; Gini coefficient; Net income; Balance (ability); Gross domestic product; Demographic economics; Index (typography); Agricultural economics; Socioeconomics; Economic growth; Inequality; Economic inequality; Demography; Gross income; Mathematics; Population; Public economics; Finance","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":[0.000827205,0.0002680703,0.0002069771,0.003567048,0.0005290383,0.0009698499,0.0001684372,0.0001197969,0.00111832],"category_scores_gemma":[0.001200865,0.0001118048,0.0002870787,0.007833321,0.0002920242,0.001062494,0.0004545479,0.0002668269,0.00009311618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002566824,"about_ca_system_score_gemma":0.002538509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06437181,"about_ca_topic_score_gemma":0.06067643,"domain_scores_codex":[0.9997826,0.00003561531,0.0000222951,0.00006647942,0.00005232316,0.0000407031],"domain_scores_gemma":[0.9996023,0.00005183929,0.0001190938,0.00001744848,0.0001693784,0.00004012353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001250777,0.00004091181,0.6577866,0.0005761801,0.0001557667,0.0004974753,0.004434879,0.009806016,0.00136654,0.03377729,0.004330984,0.2871022],"study_design_scores_gemma":[0.000005543767,0.00005486373,0.9236389,0.0001345377,0.0000724331,0.0002145934,0.002911112,0.003816778,0.0008963597,0.003042049,0.065191,0.00002177665],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9353853,0.01773582,0.005696817,0.001477754,0.00008795732,0.00004855969,0.002747656,0.00005437405,0.03676558],"genre_scores_gemma":[0.9807771,0.008795456,0.003280786,0.00004446356,0.00005925617,0.00003294616,0.001233333,0.00001093013,0.005765849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06437181,"threshold_uncertainty_score":0.1279943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0738756541371445,"score_gpt":0.260146605747242,"score_spread":0.1862709516100975,"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."}}