{"id":"W4402072935","doi":"10.5376/lgg.2024.15.0020","title":"CRISPR/Cas9 Genome Editing in Legumes: Opportunities for Functional Genomics and Breeding","year":2024,"lang":"en","type":"article","venue":"Legume Genomics and Genetics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CRISPR; Genome editing; Genomics; Functional genomics; Biology; Genome; Computational biology; Genetics; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002888801,0.0002539581,0.0002024117,0.0001388812,0.0001161858,0.0001761809,0.000103444,0.0001856087,0.000006354721],"category_scores_gemma":[0.00002096919,0.000284586,0.00007230997,0.00006260447,0.00008887957,0.000008376273,0.0001716232,0.0001319755,0.000001239577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000369144,"about_ca_system_score_gemma":0.00009798217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008361437,"about_ca_topic_score_gemma":0.00004384076,"domain_scores_codex":[0.9986851,0.00001417034,0.0003249425,0.0005101873,0.00008467815,0.0003809384],"domain_scores_gemma":[0.9995568,0.00003581986,0.00003853257,0.0001861639,0.00004930981,0.0001333404],"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":[0.00005362637,0.00002938054,0.001507108,0.0004793601,0.0001642943,0.00002457994,0.0007973911,0.005376485,0.9533883,0.001245048,0.001251448,0.03568295],"study_design_scores_gemma":[0.001532829,0.000666072,0.007843222,0.0001011828,0.0001611792,0.0003274543,0.003491108,0.04415787,0.06225336,0.001437636,0.876741,0.001287093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9309934,0.02940913,0.03764219,0.0004874097,0.0006851851,0.0003135553,0.000142968,0.00002547372,0.0003006509],"genre_scores_gemma":[0.9846258,0.007413237,0.005129329,0.0002493629,0.001568219,0.00004847885,0.0001884375,0.00008049343,0.0006966979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.891135,"threshold_uncertainty_score":0.9999606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02847308651354584,"score_gpt":0.2777563198513749,"score_spread":0.249283233337829,"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."}}