{"id":"W4283074872","doi":"10.1101/2022.06.17.496490","title":"Rapid, scalable, combinatorial genome engineering by Marker-less Enrichment and Recombination of Genetically Engineered loci (MERGE)","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Arts and Sciences; National Institutes of Health; Army Research Office; University of Toronto; York University; Fonds de recherche du Québec – Nature et technologies; Welch Foundation; University of Washington; Concordia University; Harvard University","keywords":"CRISPR; Biology; Genome editing; Genetics; Computational biology; Genome engineering; Genome; Cas9; Merge (version control); Gene; Homologous recombination; Locus (genetics); Computer science","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":[0.0006400455,0.0007691826,0.0006758792,0.0007073575,0.000284036,0.0009997645,0.0008226846,0.0004855762,0.0009373655],"category_scores_gemma":[0.0003744508,0.0004409949,0.0007866386,0.0004562574,0.000487923,0.0005316995,0.00161434,0.001328305,0.0009151123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003132925,"about_ca_system_score_gemma":0.0003763076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000295967,"about_ca_topic_score_gemma":0.00049223,"domain_scores_codex":[0.9993284,0.00007575203,0.00006552267,0.0001693084,0.0002808298,0.00008032133],"domain_scores_gemma":[0.9996575,0.00004393187,0.0001255475,0.00008298317,0.00003132602,0.00005867408],"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":[0.00004794197,0.00003834516,0.0001609199,0.00005834667,0.00001575935,0.0000581635,0.00001554009,0.0006364135,0.994139,0.000671878,0.0001434063,0.004014252],"study_design_scores_gemma":[0.00001469432,0.0001222454,0.0003756937,0.000005992554,0.00001356183,0.0001948755,0.00001227072,0.002803142,0.9910132,0.0001871432,0.005242254,0.00001486343],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4407221,0.001658084,0.5462823,0.0003197106,0.0001543299,0.0006621671,0.001672473,0.004578899,0.003949984],"genre_scores_gemma":[0.7001842,0.001296755,0.2893948,0.0001552157,0.00003079337,0.00045207,0.002675023,0.0004709422,0.005340221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009997645,"threshold_uncertainty_score":0.003384948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005339206555632564,"score_gpt":0.2121005421229024,"score_spread":0.2067613355672699,"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."}}