{"id":"W3179509384","doi":"10.3390/plants10071423","title":"OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security","year":2021,"lang":"en","type":"review","venue":"Plants","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Higher Education of the Russian Federation; Russian Science Foundation; Grantová Agentura České Republiky; Deutsche Forschungsgemeinschaft","keywords":"Food security; Agriculture; Microbiome; Green Revolution; Biotechnology; Population; Emerging technologies; Biology; Genotyping; Computer science; Risk analysis (engineering); Data science; Natural resource economics; Ecology; Bioinformatics; Business; Environmental health; Artificial intelligence; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001355807,0.0002225229,0.0005143781,0.00004616478,0.0001253576,0.00005527059,0.0001018807,0.0003035124,0.000005352398],"category_scores_gemma":[0.0000496868,0.0002158432,0.000114167,0.00001918322,0.0000536552,0.000001387844,0.0001713714,0.00008952266,7.347764e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005277479,"about_ca_system_score_gemma":0.00006094554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002360794,"about_ca_topic_score_gemma":0.000006054977,"domain_scores_codex":[0.9990641,0.00003633322,0.0002107245,0.0004171724,0.00006772518,0.0002039172],"domain_scores_gemma":[0.9995854,0.00003992312,0.0001148022,0.0001250153,0.00004677435,0.0000880076],"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":[0.00005283951,0.0003018247,0.0002490896,0.03541289,0.001550152,0.00002880448,0.0001758055,3.065184e-7,0.002920966,0.000692452,0.01978347,0.9388314],"study_design_scores_gemma":[0.0001409942,0.0001560455,0.000009699083,0.001013354,0.0001268772,0.0001186099,0.00004145873,0.000001072882,0.0002800736,0.0001226637,0.997741,0.0002481214],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0007876412,0.9922044,0.0001152619,0.00001533653,0.0001230981,0.0003368425,0.006239798,0.00001030558,0.0001673349],"genre_scores_gemma":[0.0003293438,0.9936629,0.001518474,0.00004837658,0.0008497557,0.00003085042,0.003456854,0.00001673397,0.00008673999],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9779575,"threshold_uncertainty_score":0.8801832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03216062009433898,"score_gpt":0.2783383793858355,"score_spread":0.2461777592914965,"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."}}