{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001111323,0.0002599883,0.0004387665,0.0006743151,0.00006693313,0.00003821058,0.0005359183,0.0007699564,0.0001994926],"category_scores_gemma":[0.000587096,0.0002841181,0.0001394993,0.0006007668,0.000278734,0.000005338816,0.0004366391,0.0009899761,0.00001703302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000112163,"about_ca_system_score_gemma":0.0004827478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003580432,"about_ca_topic_score_gemma":0.0002571664,"domain_scores_codex":[0.9974356,0.0002308727,0.000531024,0.0006091376,0.0005119952,0.0006813917],"domain_scores_gemma":[0.9987121,0.00004518262,0.00007921719,0.0006972739,0.0003525881,0.0001137034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004344848,0.0004676956,0.0009097693,0.0008903406,0.0001830364,0.0001133247,0.001046338,0.008120481,0.4248185,0.00004402035,0.5608894,0.002082592],"study_design_scores_gemma":[0.009132821,0.002425613,0.009268014,0.002860739,0.0001131328,0.0001236626,0.003235109,0.07301232,0.3510574,0.000938881,0.5450174,0.002814872],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8688335,0.0135185,0.01408581,0.0007369303,0.009658159,0.003233884,0.0003878275,0.00006579,0.0894796],"genre_scores_gemma":[0.9234684,0.002605111,0.02346482,0.00008970991,0.0033286,0.0001677127,0.001364491,0.0004041431,0.04510699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07376109,"threshold_uncertainty_score":0.9999611,"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."}}