{"id":"W2242716857","doi":"","title":"Multifactor Productivity Measurement at Statistics Canada","year":2013,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; Multifactor productivity; Computer science; Econometrics; Data science; Economics; Economic growth; Total factor productivity","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":[0.006375291,0.001069481,0.0009415882,0.008331116,0.003762461,0.004807226,0.001867191,0.0008169034,0.03164062],"category_scores_gemma":[0.02790634,0.0007140015,0.0006663736,0.02213957,0.00107275,0.001947148,0.002027659,0.002316465,0.01053949],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0668043,"about_ca_system_score_gemma":0.1652907,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9822266,"about_ca_topic_score_gemma":0.9724386,"domain_scores_codex":[0.9812041,0.001608054,0.0005742379,0.00138832,0.01327568,0.001949602],"domain_scores_gemma":[0.9328017,0.003693637,0.001894329,0.003342384,0.05558211,0.002685828],"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":[0.0001550252,0.0001053457,0.02721961,0.0003301977,0.00007862368,0.0001148351,0.00109295,0.00373983,0.001153317,0.08916399,0.6907592,0.1860871],"study_design_scores_gemma":[0.0000533917,0.00003590653,0.07417363,0.0002795027,0.00003604012,0.00007020403,0.0008296299,0.009917422,0.002821136,0.01046028,0.9011713,0.0001514992],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03590614,0.00505502,0.09270774,0.01837549,0.001745368,0.001016939,0.4925941,0.01399529,0.3386039],"genre_scores_gemma":[0.2730033,0.006253952,0.1378517,0.002663188,0.0004923183,0.001271306,0.2721462,0.003744494,0.3025736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9331957,"threshold_uncertainty_score":0.4847014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07368035424852179,"score_gpt":0.2637401421679922,"score_spread":0.1900597879194705,"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."}}