{"id":"W3109225371","doi":"10.1063/5.0029846","title":"Is microfluidics the “assembly line” for CRISPR-Cas9 gene-editing?","year":2020,"lang":"en","type":"article","venue":"Biomicrofluidics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"CRISPR; Genome editing; Microfluidics; Cas9; Assembly line; Line (geometry); Nanotechnology; Computational biology; Computer science; Gene; Biology; Genetics; Engineering; Materials 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001939124,0.0002741734,0.0001969008,0.00002708553,0.0001904597,0.00006643763,0.0004605025,0.0002237274,0.00001328987],"category_scores_gemma":[0.0001521208,0.0002308152,0.0002440762,0.0001470441,0.00008614438,0.000003533467,0.0001869531,0.0001144984,0.00003346967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001335523,"about_ca_system_score_gemma":0.00008761316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008052819,"about_ca_topic_score_gemma":9.880085e-7,"domain_scores_codex":[0.9985856,0.00002555691,0.0003315048,0.0004997539,0.0001337179,0.0004238713],"domain_scores_gemma":[0.9991536,0.00002787361,0.0000766216,0.0004327168,0.0001364028,0.000172847],"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":[0.00004473229,0.00001583092,0.0001612793,0.00003634187,0.00005965177,0.000001347486,0.0001572199,0.000007398077,0.7989358,0.00002575691,0.1990707,0.001483985],"study_design_scores_gemma":[0.0003936008,0.0001454557,0.00005134077,0.000004098397,0.00002700393,0.00000982042,0.00007532344,0.0002464873,0.5760025,0.000009020886,0.4228694,0.000165938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5520209,0.03949491,0.3940031,0.01183858,0.0009984068,0.0009060435,0.0004796993,0.00009006204,0.000168289],"genre_scores_gemma":[0.9746979,0.004534394,0.003736521,0.01241227,0.003739455,0.00008004069,0.0002894382,0.0001293955,0.0003806163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.422677,"threshold_uncertainty_score":0.941237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02543690408645854,"score_gpt":0.3128586048609039,"score_spread":0.2874217007744454,"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."}}