{"id":"W3177406927","doi":"10.21926/obm.genet.2003115","title":"Breeding “CRISPR” Crops<a class=\"tippyShow\" data-tippy-interactive=\"true\" data-tippy-arrow=\"true\" data-tippy-theme=\"light-border\" style=\"cursor:pointer\" data-tippy-content=\"<p style=text-indent:0in;>In loving memory of my beloved wife, Jean Georges.</p>\"><sup>1</sup></a>","year":2020,"lang":"en","type":"article","venue":"OBM Genetics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Quest University Canada","funders":"","keywords":"Food security; Face (sociological concept); CRISPR; Argument (complex analysis); Environmental ethics; Economic shortage; Humanity; Risk analysis (engineering); Computer science; Law and economics; Business; Political science; Sociology; Agriculture; Law; Ecology; Social science; 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":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","open_science","research_integrity"],"category_scores_codex":[0.003598209,0.003741882,0.003851686,0.001079021,0.0008226131,0.00104288,0.01690045,0.002027853,0.001065184],"category_scores_gemma":[0.003386455,0.004072611,0.000792416,0.001986811,0.001103908,0.0007995927,0.02012022,0.002415162,0.00037073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004512694,"about_ca_system_score_gemma":0.001189392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002248141,"about_ca_topic_score_gemma":0.003543429,"domain_scores_codex":[0.9774252,0.000998983,0.005251742,0.008617676,0.00328107,0.004425336],"domain_scores_gemma":[0.9762569,0.0008149326,0.002296002,0.01730078,0.00123732,0.002094082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001579779,0.001299905,0.002103199,0.001160923,0.002483157,0.0004178345,0.001454883,0.004300461,0.8706861,0.0001976436,0.09627521,0.01804097],"study_design_scores_gemma":[0.00968201,0.00160191,0.002963756,0.000940304,0.001977797,0.0003705634,0.001605376,0.1554335,0.1288989,0.00004189277,0.6910993,0.005384568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8009787,0.06372155,0.06147884,0.006798228,0.007186212,0.007713275,0.04369634,0.00119259,0.007234319],"genre_scores_gemma":[0.9245567,0.01060857,0.01303035,0.001516219,0.004275321,0.0001396872,0.04132152,0.001276928,0.003274682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7417871,"threshold_uncertainty_score":0.9999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03361825767792399,"score_gpt":0.3015865270747126,"score_spread":0.2679682693967886,"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."}}