{"id":"W3211876746","doi":"10.1016/bs.mie.2021.08.012","title":"A detection method for the capture of genomic signatures: From disease diagnosis to genome editing","year":2021,"lang":"en","type":"article","venue":"Methods in enzymology on CD-ROM/Methods in enzymology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Amplicon; Computational biology; Genome; genomic DNA; Biology; Genome editing; Genetics; DNA sequencing; CRISPR; Computer science; DNA; Polymerase chain reaction; Gene","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":[0.001069705,0.001133666,0.0008630239,0.001471219,0.000476371,0.001341253,0.001373083,0.002688597,0.002490129],"category_scores_gemma":[0.001628908,0.0008935022,0.0007043378,0.0006365867,0.0008947127,0.001021083,0.001569803,0.002841787,0.002629236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00041603,"about_ca_system_score_gemma":0.0004672124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003355591,"about_ca_topic_score_gemma":0.0006418268,"domain_scores_codex":[0.9985129,0.0001746008,0.00005757296,0.0006078329,0.000518893,0.0001281837],"domain_scores_gemma":[0.9986975,0.0006075105,0.0001712876,0.0001913185,0.0001679959,0.0001642989],"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":[0.00004025542,0.00003429484,0.0001923751,0.0001359151,0.00001775281,0.00006147898,0.00002711782,0.000070013,0.977152,0.0007062413,0.0007696809,0.02079277],"study_design_scores_gemma":[0.00001395278,0.0001163364,0.0006203462,0.00002251916,0.00002536917,0.0007671094,0.00001756758,0.003301941,0.9872448,0.0006434075,0.007191806,0.000034739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03767652,0.004081582,0.9491425,0.000933975,0.0004144456,0.0002115401,0.0007153807,0.002685704,0.00413833],"genre_scores_gemma":[0.2255572,0.005469252,0.7439546,0.002022501,0.0002025412,0.000653779,0.001637107,0.0005409319,0.01996201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002688597,"threshold_uncertainty_score":0.008330345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047492559742413,"score_gpt":0.4035905245030221,"score_spread":0.3831155989055979,"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."}}