{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009193201,0.0001163402,0.0001271053,0.00006263424,0.0001033755,0.0000194924,0.0003880663,0.0003836334,0.000006714255],"category_scores_gemma":[0.00008857436,0.0001259706,0.00006324302,0.00005299381,0.0001057161,0.000002059636,0.000200554,0.00008975556,0.000001576278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001569571,"about_ca_system_score_gemma":0.000009463854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001052794,"about_ca_topic_score_gemma":0.0004152198,"domain_scores_codex":[0.9992352,0.000005180372,0.0001332502,0.0002998175,0.00003731733,0.0002892638],"domain_scores_gemma":[0.9993351,0.000002684532,0.00005972512,0.0005570023,0.00001593899,0.00002955853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000173274,0.0002089369,0.01616464,0.0000921142,0.0001923064,0.00001729228,0.00008759085,0.0004064146,0.73212,0.02697432,0.001177685,0.2223854],"study_design_scores_gemma":[0.002928809,0.00169662,0.05754554,0.00002162575,0.00003039306,0.00007444698,0.0001077771,0.0007293906,0.7429744,0.01031511,0.1828675,0.0007083758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849272,0.0007724238,0.009821244,0.001159094,0.0004026074,0.0001052427,0.000083658,0.0000488452,0.002679701],"genre_scores_gemma":[0.9976927,0.00005340518,0.00176536,0.00002692954,0.000254822,0.00002811113,0.0000541855,0.00001144515,0.0001130661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.221677,"threshold_uncertainty_score":0.5136934,"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."}}