{"id":"W4240274708","doi":"10.1093/nar/gky512","title":"Optimized knock-in of point mutations in zebrafish using CRISPR/Cas9","year":2018,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Appili Therapeutics (Canada); McMaster University; Dalhousie University","funders":"","keywords":"CRISPR; Biology; Cas9; Zebrafish; Genetics; Gene knockin; Point mutation; Computational biology; Palindrome; Genome editing; Polymerase chain reaction; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007055263,0.00009075193,0.0001458354,0.0002621617,0.00004936321,0.00001614066,0.0002120091,0.0001287056,0.0001071931],"category_scores_gemma":[0.0002901965,0.00009623173,0.00004617009,0.0004250328,0.0001955781,0.00000484536,0.0001728855,0.0001888829,0.00001157973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000036844,"about_ca_system_score_gemma":0.00009637074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00019829,"about_ca_topic_score_gemma":0.0001256856,"domain_scores_codex":[0.9987771,0.0001081782,0.0002500458,0.0002688954,0.0002223192,0.0003734695],"domain_scores_gemma":[0.9993805,0.0000308176,0.00002263858,0.0003157632,0.0001852263,0.00006504825],"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":[0.0001116541,0.00009012783,0.003297483,0.00003092264,0.00001364928,0.000008576483,0.0004152116,0.003205878,0.9910789,0.00005297117,0.0004081768,0.00128644],"study_design_scores_gemma":[0.001913346,0.0004522899,0.01490629,0.00008573558,0.000005587871,0.00001587872,0.0009648557,0.02743559,0.9511191,0.0002715308,0.002597864,0.0002319549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906629,0.0002321993,0.006324337,0.0001062645,0.00006107421,0.0002020383,0.000006647078,0.000005537494,0.002398965],"genre_scores_gemma":[0.9905899,0.00004218867,0.009054773,0.00001857126,0.0001180095,0.00001233144,0.00001195474,0.00002283235,0.0001295034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03995983,"threshold_uncertainty_score":0.3924216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03848736674112608,"score_gpt":0.4032562825176894,"score_spread":0.3647689157765633,"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."}}