{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002275994,0.0004553627,0.0005241242,0.0000785944,0.00004230904,0.00001430888,0.0006685116,0.000545881,0.0001088456],"category_scores_gemma":[0.0001341043,0.0005101435,0.000383271,0.0001164237,0.00008445032,0.000003963305,0.001993139,0.0005088036,0.00005723599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002638238,"about_ca_system_score_gemma":0.0001664759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001029964,"about_ca_topic_score_gemma":0.000005714308,"domain_scores_codex":[0.9975688,0.00008288669,0.0005745112,0.001140354,0.0002650878,0.0003683965],"domain_scores_gemma":[0.9980227,0.00000715615,0.000214766,0.001304402,0.0002459205,0.0002050093],"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":[0.0001166282,0.000075533,0.005334941,0.0002862109,0.0003094393,0.00001093916,0.0001361572,0.06569754,0.9277252,0.0000991757,0.0001208154,0.00008744248],"study_design_scores_gemma":[0.0004224814,0.00009957519,0.01256846,0.00006045441,0.0001159254,0.000007472309,0.00003605303,0.005861367,0.9774587,0.0003681552,0.0024909,0.0005104269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758353,0.001209272,0.02068447,0.0001814431,0.0003505551,0.0004324408,0.00009768997,0.00006136645,0.001147508],"genre_scores_gemma":[0.9966648,0.0004954647,0.001615129,0.00006916664,0.0004568434,0.00007854453,0.000361463,0.00008703157,0.0001715323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05983618,"threshold_uncertainty_score":0.999735,"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."}}