{"id":"W2719966376","doi":"10.1007/978-1-4939-7128-2_20","title":"Genome Editing of C. elegans","year":2017,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Precursory Research for Embryonic Science and Technology; Japan Science and Technology Agency; University of British Columbia","keywords":"Caenorhabditis elegans; Genome; Biology; Caenorhabditis; Genome editing; Model organism; Computational biology; Genetic screen; Genetics; Evolutionary biology; Gene; Phenotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005746349,0.0007467008,0.0004923269,0.0008719445,0.0005941061,0.000656958,0.00105339,0.0008335154,0.004070804],"category_scores_gemma":[0.0005464483,0.0004632406,0.0006255124,0.0003112633,0.0004737348,0.0002770799,0.0005630723,0.00144029,0.0009238471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005180738,"about_ca_system_score_gemma":0.0006494911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00161449,"about_ca_topic_score_gemma":0.003985055,"domain_scores_codex":[0.9994859,0.00005242624,0.00005364995,0.0001513608,0.0001817767,0.00007484831],"domain_scores_gemma":[0.9995114,0.0001337236,0.00009568971,0.000112073,0.00005686511,0.00009038558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001749425,0.00004101714,0.000137855,0.0001683482,0.00002565869,0.0002476051,0.0000476451,0.0004947992,0.9831492,0.002654186,0.000769589,0.01208905],"study_design_scores_gemma":[0.00006362723,0.00008023172,0.001429958,0.00004146349,0.00007520465,0.0004323239,0.00002183497,0.003079078,0.9557577,0.0005477262,0.03844161,0.00002932422],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6588789,0.004201134,0.284969,0.001089732,0.001985331,0.001161485,0.01004248,0.008870806,0.02880109],"genre_scores_gemma":[0.8551577,0.002038108,0.1039222,0.0004207644,0.00009042744,0.0005149284,0.004641711,0.001951307,0.03126297],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004070804,"threshold_uncertainty_score":0.01361823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02244306672829433,"score_gpt":0.3726417894763182,"score_spread":0.3501987227480238,"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."}}