{"id":"W3003386920","doi":"10.1007/978-1-0716-0290-4_21","title":"Genome Editing in Zebrafish Using High-Fidelity Cas9 Nucleases: Choosing the Right Nuclease for the Task","year":2020,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"AGADA Biosciences; Dalhousie University; Agricultural Research Institute of Ontario","funders":"","keywords":"Cas9; Genome editing; CRISPR; Computational biology; Biology; Zebrafish; Mutagenesis; Nuclease; Genome; Genetics; Computer science; Mutation; 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":[0.001462763,0.0005154199,0.0008909391,0.0003589475,0.000667359,0.001607369,0.001064872,0.001482879,0.002406821],"category_scores_gemma":[0.001179336,0.0005676286,0.0007057879,0.0003030523,0.001159966,0.001344462,0.0009357473,0.00257174,0.001035642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163202,"about_ca_system_score_gemma":0.001241227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002853436,"about_ca_topic_score_gemma":0.006909323,"domain_scores_codex":[0.9993713,0.00005782206,0.00004812003,0.0001416053,0.0002820621,0.00009896167],"domain_scores_gemma":[0.999561,0.0001081961,0.0001031798,0.00006550943,0.00007253072,0.00008949928],"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":[0.0001573966,0.00001958112,0.0004434631,0.0002418243,0.00003402902,0.0001845612,0.00006169386,0.0005714031,0.9731686,0.005664006,0.001342475,0.01811099],"study_design_scores_gemma":[0.00006931497,0.000204575,0.002628852,0.0001532684,0.000109494,0.000989988,0.0001460837,0.005651545,0.9471326,0.006143421,0.03664936,0.0001213707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2943804,0.02075644,0.6475827,0.01172907,0.001747629,0.0005198123,0.001616664,0.00360636,0.0180609],"genre_scores_gemma":[0.6154625,0.01203444,0.3497646,0.002401648,0.0001317491,0.0001793372,0.0009137639,0.0009329253,0.01817901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002853436,"threshold_uncertainty_score":0.00843966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02022655046335211,"score_gpt":0.3906998122064582,"score_spread":0.3704732617431061,"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."}}