{"id":"W2102903807","doi":"","title":"Canada’s Productivity Performance in International Perspective","year":2016,"lang":"en","type":"article","venue":"","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Productivity; Regional science; Computer science; Economics; Sociology; Economic growth; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000359812,0.00008413535,0.0001627548,0.0001124247,0.0000281428,0.00001262964,0.0001737872,0.00002886635,0.0008113052],"category_scores_gemma":[0.000207654,0.00007341176,0.00002408626,0.00007755098,0.00003334646,0.0004144777,0.00004275315,0.00006584184,0.0002012734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007723438,"about_ca_system_score_gemma":0.0001658894,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3901286,"about_ca_topic_score_gemma":0.5326917,"domain_scores_codex":[0.9991244,0.000006727631,0.0002429471,0.0004058303,0.00002047686,0.000199602],"domain_scores_gemma":[0.9995961,0.00002717389,0.00009399988,0.000211524,0.00002824483,0.00004291735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001574651,0.00003381451,0.740541,0.00000284188,0.00001206203,0.000001066745,0.00007199976,0.000004469946,0.0000273135,0.2560186,0.001232538,0.002038472],"study_design_scores_gemma":[0.0004641437,0.00001841189,0.8599645,0.000006465216,4.23709e-7,0.000003997948,0.00004781332,0.0001189825,0.0008254574,0.023488,0.1148546,0.0002072675],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7058248,0.0001029613,0.000224674,0.02196502,0.0008042104,0.00009705114,0.00006093116,0.00001495348,0.2709054],"genre_scores_gemma":[0.9913035,0.00004159609,0.0001040686,0.000162907,0.000169628,0.00001186627,9.636007e-7,0.000007546754,0.008197926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2854787,"threshold_uncertainty_score":0.8883223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849587306452863,"score_gpt":0.190095258496278,"score_spread":0.1715993854317494,"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."}}