{"id":"W2738000304","doi":"10.1108/caer-02-2017-0028","title":"Promise, problems and prospects: agri-biotech governance in China, India and Japan","year":2017,"lang":"en","type":"article","venue":"China Agricultural Economic Review","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Affairs Canada","funders":"","keywords":"Corporate governance; Incentive; Scarcity; China; Business; Agriculture; Biotechnology; Agricultural biotechnology; Population; International trade; Economic growth; Economics; Political science; Market economy; Finance; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001389145,0.0001900636,0.0002826978,0.000007691679,0.0001016114,0.00008222921,0.000198869,0.00008749437,0.0000107059],"category_scores_gemma":[0.00004510413,0.0001313528,0.00004790078,0.00001637792,0.00005685967,0.00001688138,0.0001662867,0.0001104081,0.000005436573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002437017,"about_ca_system_score_gemma":0.00001270295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007294917,"about_ca_topic_score_gemma":0.0001273697,"domain_scores_codex":[0.999153,0.0000122642,0.0002381872,0.0003770229,0.00003131447,0.0001881904],"domain_scores_gemma":[0.999456,0.000002214915,0.0001616289,0.000299497,0.000007424514,0.00007322724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000771489,0.0002724271,0.2005271,0.02173132,0.0004271327,0.00002261556,0.001215558,0.0003687626,0.411613,0.003724708,0.01658086,0.3434394],"study_design_scores_gemma":[0.0003666174,0.00006940007,0.9798769,0.0008235105,0.00001565284,0.00006105676,0.000005495808,0.00001932354,0.003979957,0.00004737691,0.01447716,0.0002575328],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874225,0.1092831,0.000002843769,0.0008028201,0.00007935426,0.0006394121,0.00001212828,0.000007140587,0.001750653],"genre_scores_gemma":[0.8482694,0.1509822,0.0001045329,0.00004069832,0.0001070227,0.00005418582,0.0000239222,0.000008956982,0.0004090593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7793498,"threshold_uncertainty_score":0.5356412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005167503196766125,"score_gpt":0.2432799351183651,"score_spread":0.238112431921599,"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."}}