{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220261,0.001142954,0.001364968,0.0008624343,0.001242625,0.003678682,0.002424033,0.002791849,0.004815956],"category_scores_gemma":[0.006701028,0.001041123,0.00103787,0.0005524689,0.003702594,0.00542078,0.001910177,0.00343413,0.0047383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001903975,"about_ca_system_score_gemma":0.00230339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179899,"about_ca_topic_score_gemma":0.000929799,"domain_scores_codex":[0.9967543,0.0005852442,0.0001984567,0.0006480868,0.001504815,0.0003091264],"domain_scores_gemma":[0.9958819,0.001367052,0.0006941268,0.0009959572,0.0007099874,0.0003510679],"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":[0.0005527691,0.0001977561,0.003011583,0.00203695,0.0002493542,0.0008213742,0.0007797201,0.004779923,0.363189,0.2967381,0.07215482,0.2554887],"study_design_scores_gemma":[0.0000826805,0.0006568286,0.001336636,0.0005786666,0.0001290263,0.001048078,0.0002714765,0.009832384,0.3459114,0.0431621,0.5967546,0.0002362013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02722269,0.04600257,0.7632502,0.1055889,0.01738167,0.000310214,0.001185999,0.0116108,0.02744694],"genre_scores_gemma":[0.3613141,0.05736564,0.5054361,0.03027095,0.005801334,0.000954972,0.001499489,0.001748939,0.03560853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004815956,"threshold_uncertainty_score":0.02231914,"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."}}