{"id":"W2751800749","doi":"10.1016/j.tibtech.2017.08.004","title":"Genome Editing for Global Food Security","year":2017,"lang":"en","type":"article","venue":"Trends in biotechnology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Global Institute for Water Security","funders":"","keywords":"Food security; World population; Agriculture; Genome editing; Population; Agricultural productivity; Climate change; Crop; Global population; Natural resource economics; Production (economics); Biotechnology; Business; Biology; Genome; Ecology; Economics; Gene; Environmental health; Medicine; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008231531,0.0007369094,0.0004563592,0.000430539,0.0003110945,0.001192002,0.0006592919,0.001145363,0.009883882],"category_scores_gemma":[0.0004583651,0.0001423911,0.0003911416,0.0003961614,0.0009707392,0.001308769,0.0008933243,0.001754614,0.001227928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007861044,"about_ca_system_score_gemma":0.0005085127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002778184,"about_ca_topic_score_gemma":0.0003596554,"domain_scores_codex":[0.9997677,0.00004183879,0.00001067204,0.00005994794,0.00007463153,0.00004514452],"domain_scores_gemma":[0.999799,0.00006320227,0.00003738495,0.00003408522,0.0000288009,0.00003746929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004806076,0.0001856665,0.0005600917,0.001463549,0.0001820011,0.0004190353,0.0002281825,0.002649174,0.3817303,0.1792616,0.02809018,0.4047496],"study_design_scores_gemma":[0.0001065548,0.0004608818,0.001483693,0.0003505498,0.000169497,0.000745006,0.0003153101,0.002102667,0.1198405,0.120375,0.7540088,0.00004148659],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1765879,0.3697814,0.1679355,0.07619146,0.01238741,0.0002095014,0.002024981,0.002916288,0.1919657],"genre_scores_gemma":[0.6677634,0.2121484,0.05768412,0.008881749,0.00212902,0.0001493459,0.001744385,0.0004384729,0.04906119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009883882,"threshold_uncertainty_score":0.0330649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255395304844964,"score_gpt":0.3234914988353415,"score_spread":0.3109375457868919,"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."}}