{"id":"W3124422471","doi":"10.20944/preprints202003.0048.v1","title":"CRISPR-Generated Animal Models of Duchenne Muscular Dystrophy","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada; University of Alberta","funders":"Faculty of Medicine and Dentistry, University of Alberta; Muscular Dystrophy Canada; University of Alberta; Canadian Institutes of Health Research; Women and Children's Health Research Institute; Children's Health Research Institute","keywords":"CRISPR; Duchenne muscular dystrophy; Dystrophin; Genome editing; Neuromuscular disease; Muscular dystrophy; Computational biology; Utrophin; Medicine; Disease; Biology; Bioinformatics; Neuroscience; Genetics; Gene; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095718,0.001144625,0.001244531,0.001831777,0.000631946,0.001169747,0.00106092,0.002472268,0.007676524],"category_scores_gemma":[0.0006135628,0.0005586804,0.0009209886,0.0005749398,0.0008197973,0.000588008,0.0007655931,0.002349975,0.003352063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008167552,"about_ca_system_score_gemma":0.0007642108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054442,"about_ca_topic_score_gemma":0.001749178,"domain_scores_codex":[0.998969,0.0001610695,0.0001354195,0.0002159529,0.0004064758,0.0001121642],"domain_scores_gemma":[0.9995108,0.0001480896,0.0001051897,0.00007231656,0.00007089552,0.00009276364],"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":[0.0004427018,0.0002738484,0.0005543385,0.00154914,0.0001525877,0.001393093,0.0002138179,0.001112727,0.9446256,0.01074824,0.008203941,0.03073007],"study_design_scores_gemma":[0.0003751644,0.001747879,0.003735075,0.000847202,0.0004756748,0.007804966,0.0002080479,0.003797602,0.6389753,0.004376789,0.3375106,0.0001457057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3348233,0.07252327,0.4319006,0.005301808,0.003910507,0.002667678,0.03938626,0.02021622,0.08927042],"genre_scores_gemma":[0.5539494,0.06452724,0.2227873,0.002737585,0.0003534843,0.004889462,0.03456565,0.002353319,0.1138366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007676524,"threshold_uncertainty_score":0.02568048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0701490884528195,"score_gpt":0.3455405255388271,"score_spread":0.2753914370860077,"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."}}