{"id":"W2883082371","doi":"","title":"Pittsburgh — Strong GDP Growth despite Little Employment Growth","year":2016,"lang":"en","type":"article","venue":"","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Unemployment; Quarter (Canadian coin); Pace; Great recession; Unemployment rate; Recession; Per capita; Demographic economics; Labour economics; Development economics; Geography; Economic growth; Keynesian economics; Demography; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005156465,0.0003148159,0.0002833769,0.0008184438,0.001063837,0.002591227,0.0003824824,0.0003743685,0.01345752],"category_scores_gemma":[0.002804072,0.0002364163,0.0001585505,0.002174254,0.0006577602,0.001880068,0.002351175,0.001091367,0.005122307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001748305,"about_ca_system_score_gemma":0.003385557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0549628,"about_ca_topic_score_gemma":0.1077366,"domain_scores_codex":[0.9996287,0.0000263396,0.00002151004,0.00009757143,0.0001251836,0.0001005638],"domain_scores_gemma":[0.9983708,0.00010598,0.0002235456,0.0001195845,0.0006448544,0.0005352807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002910592,0.0001055436,0.1626845,0.000394891,0.00009184602,0.001747701,0.001353657,0.0008804955,0.001565174,0.02675805,0.6719568,0.1321703],"study_design_scores_gemma":[0.00004471084,0.000103869,0.2759956,0.000183489,0.00002914761,0.0005156564,0.002176693,0.001330737,0.001495416,0.005031481,0.7130615,0.00003186727],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4918784,0.003295887,0.004047774,0.07796936,0.00212646,0.0001638507,0.04397009,0.002454733,0.3740934],"genre_scores_gemma":[0.8956712,0.002600632,0.003284388,0.005127499,0.0008101439,0.0001250914,0.0240395,0.0004887306,0.06785277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0549628,"threshold_uncertainty_score":0.1092857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03763419925551204,"score_gpt":0.2108912091795883,"score_spread":0.1732570099240762,"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."}}