{"id":"W3185130270","doi":"","title":"Forecasting Canadian GDP Growth with Machine Learning","year":2021,"lang":"en","type":"article","venue":"Carleton Economic Papers","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Sample (material); Variable (mathematics); Real gross domestic product; Econometrics; Computer science; Artificial intelligence; Data set; Term (time); Machine learning; Economics; Mathematics","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":[0.0006123867,0.0007292781,0.0003715715,0.001458639,0.0005700763,0.00107026,0.0007047447,0.0003451211,0.001451979],"category_scores_gemma":[0.002740523,0.0002346427,0.0005292055,0.002626341,0.0003090742,0.0004888531,0.0004139286,0.0006438758,0.0004168061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01056564,"about_ca_system_score_gemma":0.009663542,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9482048,"about_ca_topic_score_gemma":0.9416196,"domain_scores_codex":[0.9996775,0.00004219889,0.00001157924,0.00005964987,0.0001472729,0.00006179784],"domain_scores_gemma":[0.9994553,0.0001247477,0.00005427479,0.00003322932,0.0002935565,0.00003884249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008315183,0.00003666433,0.03064575,0.00006562331,0.00005499435,0.00007372319,0.00003923749,0.8824707,0.0004400336,0.003128509,0.009859794,0.07310188],"study_design_scores_gemma":[0.000005395632,0.000005408424,0.008948091,0.000008557636,0.000007558182,0.00000419512,0.00002210231,0.9876179,0.0003986884,0.0006208817,0.0023497,0.00001152635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8584048,0.00290062,0.08514328,0.00416544,0.0002769759,0.0001228785,0.01726624,0.003656328,0.02806345],"genre_scores_gemma":[0.9640572,0.0008521468,0.023185,0.00009612202,0.00004127358,0.00003877818,0.008022757,0.00005932572,0.003647379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05179524,"threshold_uncertainty_score":0.1042005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07171066533869278,"score_gpt":0.28833728777517,"score_spread":0.2166266224364772,"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."}}