{"id":"W4313454447","doi":"10.1038/s41587-022-01516-7","title":"CRISPR-edited plants by grafting","year":2023,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Genetics","funders":"","keywords":"CRISPR; Grafting; Biology; Computational biology; Chemistry; Genetics; Gene","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":[0.0004647627,0.001067193,0.0007833865,0.001077298,0.0005049463,0.0009856279,0.001345137,0.001313237,0.00682401],"category_scores_gemma":[0.0002640193,0.0007527357,0.0009886378,0.0006849568,0.000769128,0.0006438875,0.001254791,0.004363296,0.003903416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005919272,"about_ca_system_score_gemma":0.0004207364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006858279,"about_ca_topic_score_gemma":0.001507305,"domain_scores_codex":[0.9994389,0.00005019606,0.00005253789,0.0001666581,0.0001674,0.0001242658],"domain_scores_gemma":[0.9995858,0.0001295477,0.0000772053,0.00009220759,0.00002837025,0.00008680989],"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":[0.00006268897,0.00004005949,0.00005842658,0.00004150947,0.00001290713,0.000107934,0.00003882307,0.0001524229,0.9947298,0.001752824,0.0002985144,0.002704051],"study_design_scores_gemma":[0.00003408048,0.00006968547,0.0007775003,0.00001260904,0.00002916351,0.0003780101,0.00002539813,0.001932633,0.9795864,0.0007113194,0.01641475,0.0000283312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5600474,0.001822891,0.3588688,0.001792689,0.002259277,0.001384267,0.007723886,0.01424487,0.051856],"genre_scores_gemma":[0.8091001,0.001185095,0.09403539,0.0007355558,0.0001248523,0.0004926141,0.005466547,0.002629782,0.08623011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00682401,"threshold_uncertainty_score":0.02282864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003694196179452568,"score_gpt":0.2829332200758302,"score_spread":0.2792390238963777,"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."}}