{"id":"W2417498326","doi":"10.1111/1477-9552.12171","title":"Trends in Approval Times for Genetically Engineered Crops in the United States and the European Union","year":2016,"lang":"en","type":"article","venue":"Journal of Agricultural Economics","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Food, Agriculture and Fisheries, Biotechnology; Seventh Framework Programme; Queen's University; Queen's University Belfast; European Commission","keywords":"European union; Bureaucracy; Member state; Member states; Politics; Political science; Agricultural economics; International trade; Business; Law; Economics","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":[0.005163996,0.0002001312,0.000242213,0.00193255,0.0003498301,0.001673997,0.0004284996,0.0006453739,0.003993118],"category_scores_gemma":[0.02296244,0.0001933337,0.0005196084,0.00212762,0.0003470365,0.0009601493,0.0005939382,0.00104358,0.0008664631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001826808,"about_ca_system_score_gemma":0.001118837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01313411,"about_ca_topic_score_gemma":0.01298789,"domain_scores_codex":[0.9960438,0.0009734929,0.0005498307,0.0008187573,0.001191465,0.00042274],"domain_scores_gemma":[0.9608924,0.0127437,0.01381424,0.00105333,0.01000572,0.001490512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001878343,0.0002787344,0.7861197,0.0005279723,0.0002614692,0.0004293584,0.003165095,0.00764617,0.005816617,0.005877763,0.0269551,0.1610438],"study_design_scores_gemma":[0.00003166898,0.0003624824,0.9480672,0.0001386253,0.00005405716,0.000309376,0.001659694,0.00271334,0.002592944,0.000496655,0.04352342,0.00005050295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674698,0.002858901,0.002320059,0.001947557,0.0001518711,0.00005707765,0.003677247,0.0001750331,0.02134237],"genre_scores_gemma":[0.9879169,0.0009421523,0.002103544,0.0003588093,0.00005593602,0.00006680971,0.003756456,0.00006050266,0.004738847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01313411,"threshold_uncertainty_score":0.02731013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01812615643147513,"score_gpt":0.210378573049326,"score_spread":0.1922524166178508,"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."}}