{"id":"W2898793048","doi":"10.3390/su10113974","title":"International R&D Spillovers and Innovation Efficiency","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Brock University; National Natural Science Foundation of China","keywords":"Productivity; Economics; Stock (firearms); Panel data; Frontier; Stochastic frontier analysis; Sustainable development; Industrial organization; International trade; Production (economics); Econometrics; Microeconomics; Macroeconomics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004550823,0.0004403802,0.0008736661,0.004800347,0.000316772,0.00263904,0.0002991217,0.0005177063,0.003837819],"category_scores_gemma":[0.01461762,0.0002321743,0.001366657,0.005578457,0.0009542938,0.002191729,0.00212282,0.0008093262,0.0007480551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149801,"about_ca_system_score_gemma":0.0008639247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002725528,"about_ca_topic_score_gemma":0.002216499,"domain_scores_codex":[0.9979483,0.0004739881,0.0002328281,0.0004566468,0.0005111648,0.0003770479],"domain_scores_gemma":[0.9773741,0.01053861,0.008252321,0.001905638,0.001349003,0.0005804106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002313602,0.0002888383,0.8179229,0.0005464998,0.001269125,0.0005410492,0.0004338198,0.06584541,0.001999836,0.03029853,0.001785206,0.07883754],"study_design_scores_gemma":[0.00003363325,0.0002247703,0.955787,0.0002205414,0.0005476009,0.0003575164,0.0006541089,0.01022666,0.004036757,0.01717536,0.01068007,0.00005601585],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9437329,0.005636323,0.01389721,0.0007637535,0.0000335502,0.00007996503,0.003669228,0.000128243,0.03205885],"genre_scores_gemma":[0.9948153,0.001279364,0.001008322,0.00006008947,0.00003204295,0.00002373752,0.001470813,0.000007769214,0.001302632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004800347,"threshold_uncertainty_score":0.0240674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02451592264501324,"score_gpt":0.2552591852349617,"score_spread":0.2307432625899484,"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."}}