{"id":"W4310500277","doi":"10.1007/978-981-19-5642-3_2","title":"Online Databases of Genome Editing in Cardiovascular and Metabolic Diseases","year":2022,"lang":"en","type":"article","venue":"Advances in experimental medicine and biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Children's Hospital of Eastern Ontario","funders":"","keywords":"Genome editing; CRISPR; Computer science; Population; Computational biology; Gene; Bioinformatics; Data science; Biology; Medicine; Genetics","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":[0.001187389,0.001597849,0.001433347,0.007185673,0.0005227906,0.002340767,0.001921169,0.001848744,0.06594514],"category_scores_gemma":[0.005826317,0.0005090233,0.0008019567,0.009221926,0.0002628636,0.001436611,0.001585474,0.001128963,0.04286926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008012085,"about_ca_system_score_gemma":0.001620528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004051228,"about_ca_topic_score_gemma":0.005241876,"domain_scores_codex":[0.9992645,0.0001079609,0.000183011,0.0001397032,0.0002125655,0.00009225305],"domain_scores_gemma":[0.9961162,0.001632771,0.0007275234,0.0006290342,0.0004176003,0.0004768842],"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":[0.003297116,0.000345101,0.007463571,0.01757289,0.0006416013,0.0009269308,0.0003144777,0.003595227,0.01910121,0.009416226,0.8083726,0.1289531],"study_design_scores_gemma":[0.0004316948,0.000164023,0.01193866,0.00165791,0.0005370254,0.001002617,0.0001354492,0.002579205,0.01334676,0.008428686,0.9596509,0.0001269868],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.003425915,0.005231038,0.003348043,0.0003617882,0.00008965964,0.00007234974,0.9702411,0.009897759,0.007332365],"genre_scores_gemma":[0.01417486,0.004715985,0.006989147,0.0003678376,0.00005739363,0.0001458533,0.9701203,0.001127881,0.002300637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06594514,"threshold_uncertainty_score":0.2206085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489438208289853,"score_gpt":0.3609337807385113,"score_spread":0.3460393986556128,"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."}}