{"id":"W2209344393","doi":"10.1089/zeb.2015.1158","title":"A Guide to Computational Tools and Design Strategies for Genome Editing Experiments in Zebrafish Using CRISPR/Cas9","year":2015,"lang":"en","type":"article","venue":"Zebrafish","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Fondation de la recherche en santé du Nouveau-Brunswick; Canadian Imperial Bank of Commerce","keywords":"CRISPR; Cas9; Genome editing; Biology; Computational biology; Genome; Genome engineering; Genetics; Gene","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":[0.0002549945,0.0001399887,0.0001410388,0.0000623926,0.0000450498,0.0001087703,0.0001030275,0.00008081002,0.00000339353],"category_scores_gemma":[0.0001230515,0.0001585635,0.00003130464,0.00007232695,0.00002133902,0.00001493455,0.00008838086,0.00004231503,0.000001380923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002846641,"about_ca_system_score_gemma":0.0001047008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002757323,"about_ca_topic_score_gemma":0.00001780049,"domain_scores_codex":[0.9990958,0.00002583958,0.0002185004,0.0002937885,0.0001077543,0.0002583124],"domain_scores_gemma":[0.9995948,0.00002309391,0.0000351803,0.0001221355,0.00009325028,0.0001315031],"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":[0.0000821696,0.00004244055,0.0003009698,0.00003032772,0.00003489239,0.000005785967,0.0008103532,0.2278414,0.7645857,0.0001214387,0.005282906,0.0008616616],"study_design_scores_gemma":[0.009086286,0.002292397,0.008806093,0.0001932832,0.00009677189,0.0002194756,0.01427756,0.09299794,0.7415086,0.003344547,0.1247296,0.002447417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4431956,0.000333227,0.5555959,0.00007116271,0.0001298585,0.0003581036,0.00002694589,0.00001309859,0.0002761316],"genre_scores_gemma":[0.8903783,0.000002900506,0.1086598,0.0003739055,0.0003599719,0.00005662632,0.00007562301,0.00002925538,0.00006360986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4471827,"threshold_uncertainty_score":0.646603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05542883642669236,"score_gpt":0.3639308624834491,"score_spread":0.3085020260567567,"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."}}