{"id":"W7097999424","doi":"","title":"The Contribution of U.S. R&amp;amp;D Spending to Manufacturing Productivity Growth in Canada","year":2000,"lang":"en","type":"article","venue":"","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Productivity; Production (economics); Multifactor productivity; Government (linguistics); Manufacturing sector","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008331232,0.0002285468,0.0003435709,0.002340157,0.002198325,0.002993546,0.0008609306,0.0007542242,0.003556613],"category_scores_gemma":[0.007655051,0.0002443758,0.0004968326,0.004333006,0.0009289352,0.0006212795,0.001316591,0.00162057,0.0003014903],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08544155,"about_ca_system_score_gemma":0.1195234,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9978823,"about_ca_topic_score_gemma":0.9989034,"domain_scores_codex":[0.9985402,0.00008031219,0.00005632074,0.0001103937,0.0004435766,0.0007692626],"domain_scores_gemma":[0.9926633,0.0005993351,0.0007117158,0.0001041448,0.004222,0.001699633],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002948748,0.0000660271,0.8536056,0.0002078829,0.0002400472,0.0004685736,0.001675428,0.009079164,0.0004717682,0.02886117,0.04905549,0.05597392],"study_design_scores_gemma":[0.00002813577,0.00001944957,0.9568869,0.000175552,0.0002091107,0.0001179272,0.002749646,0.005835441,0.0005593117,0.001729877,0.0316489,0.00003963536],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8957239,0.00975742,0.0004335427,0.04206642,0.0002504479,0.0000364536,0.01481059,0.00009032519,0.03683093],"genre_scores_gemma":[0.9874833,0.003318221,0.0001381239,0.0004934375,0.00005466678,0.000004899324,0.001253175,0.00001676509,0.007237497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9991669,"threshold_uncertainty_score":0.6199248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266649847093623,"score_gpt":0.201990055638916,"score_spread":0.1793235571679798,"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."}}