{"id":"W2807817820","doi":"10.1093/nar/gky674","title":"Optimized knock-in of point mutations in zebrafish using CRISPR/Cas9","year":2018,"lang":"en","type":"erratum","venue":"Nucleic Acids Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Appili Therapeutics (Canada); McMaster University; Dalhousie University","funders":"","keywords":"CRISPR; Biology; Cas9; Zebrafish; Gene knockin; Point mutation; Genetics; Computational biology; Genome editing; Palindrome; Mutation; Polymerase chain reaction; 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":[0.0009664366,0.0008380209,0.0005031178,0.0009953864,0.0007182173,0.0007409267,0.000937999,0.0009602742,0.01162713],"category_scores_gemma":[0.00135013,0.0004022446,0.0004651989,0.0006282728,0.0005690421,0.0007932407,0.0007437723,0.001305007,0.00606079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094157,"about_ca_system_score_gemma":0.001266232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627467,"about_ca_topic_score_gemma":0.008633156,"domain_scores_codex":[0.9992445,0.00005537276,0.0001498985,0.0001044633,0.0003944876,0.00005130519],"domain_scores_gemma":[0.9994823,0.00009328679,0.00009494228,0.00008772464,0.0002125344,0.00002918729],"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":[0.0004539809,0.0001140372,0.001798773,0.001592936,0.00004912829,0.004904476,0.0003183044,0.002585502,0.6821089,0.01845774,0.1264852,0.1611311],"study_design_scores_gemma":[0.00005844921,0.0001929533,0.002460748,0.0001704375,0.00006096913,0.002045441,0.00009168451,0.001683069,0.4001401,0.001214874,0.5917679,0.0001133922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1467981,0.0245061,0.4914936,0.02006581,0.1274236,0.001734716,0.03795593,0.01968295,0.1303391],"genre_scores_gemma":[0.154461,0.0162359,0.4762364,0.003565574,0.00110361,0.0006288355,0.01495624,0.003040654,0.3297718],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01162713,"threshold_uncertainty_score":0.03889668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03605644548208621,"score_gpt":0.3946273896439861,"score_spread":0.3585709441618999,"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."}}