{"id":"W3126460557","doi":"10.1016/j.ymeth.2021.01.005","title":"Production of knockout mouse lines with Cas9","year":2021,"lang":"en","type":"article","venue":"Methods","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Toronto Centre for Phenogenomics","funders":"Canada Foundation for Innovation; Ontario Genomics","keywords":"Gene knockout; Knockout mouse; Germline; Biology; Transgene; CRISPR; Cas9; Genetics; Gene targeting; Gene; Cell biology; Electroporation; Computational biology","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":[0.0001478187,0.00005700334,0.00007799097,0.00001318879,0.00001356959,0.00000345699,0.00003562673,0.00004129174,0.000008896101],"category_scores_gemma":[0.0001241458,0.00004869428,0.00002791739,0.00006850353,0.00001843791,7.232341e-7,0.00002868328,0.00003054914,3.657814e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001996686,"about_ca_system_score_gemma":0.00002874523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004067183,"about_ca_topic_score_gemma":0.00001211138,"domain_scores_codex":[0.999602,0.00005121002,0.00008020455,0.000153007,0.00003993235,0.00007367565],"domain_scores_gemma":[0.999648,0.000005746409,0.0000207559,0.0002172239,0.00008687766,0.00002140556],"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":[0.00001252223,0.00001647026,0.0003351451,0.00002521941,0.00002196009,0.000001129086,0.00003258681,0.001397301,0.9824612,0.000008870864,0.0001656244,0.01552195],"study_design_scores_gemma":[0.00009325034,0.00005903038,0.0005722647,0.000005749818,0.00001263432,0.00002821353,0.00005851607,0.00005803098,0.9801409,0.000006694151,0.01890121,0.00006347131],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6788114,0.001333722,0.3192099,0.00007765321,0.0001606046,0.00004145398,0.000001587608,0.000007098478,0.0003565205],"genre_scores_gemma":[0.4044599,0.000137682,0.5894868,0.00002697613,0.0002377131,0.00000797601,0.00002346989,0.00001706118,0.005602501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2743516,"threshold_uncertainty_score":0.1985695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589447998957007,"score_gpt":0.3827329254252952,"score_spread":0.3668384454357252,"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."}}