{"id":"W2850515613","doi":"10.1039/c8lc00470f","title":"An automated microfluidic gene-editing platform for deciphering cancer genes","year":2018,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"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; Concordia University","keywords":"Gene; Genome editing; Microfluidics; Computational biology; Biology; Computer science; Genetics; CRISPR; Nanotechnology; 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":[0.00008858651,0.00014181,0.0001012415,0.00002772434,0.0001074558,0.00002735648,0.0001449054,0.0001056548,0.00002137034],"category_scores_gemma":[0.00002223047,0.000138507,0.00005187256,0.00005464774,0.00003117727,0.000003163409,0.00003142807,0.00003859928,0.000006840968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000140551,"about_ca_system_score_gemma":0.00003002044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002355783,"about_ca_topic_score_gemma":0.00006885679,"domain_scores_codex":[0.9992294,0.000006885488,0.0001357392,0.00028589,0.00006516864,0.0002768864],"domain_scores_gemma":[0.9995673,0.000007292252,0.0000344779,0.000249724,0.00006007825,0.00008111285],"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":[0.0000451321,0.00001813083,0.0006942297,0.00001796139,0.00002856207,6.841086e-7,0.00007130859,0.0002421765,0.9878323,0.0000172015,0.00132587,0.009706409],"study_design_scores_gemma":[0.0004434836,0.0003237303,0.001976928,0.00001891009,0.00001324615,0.000006089911,0.00003388342,0.005001207,0.957654,0.00001145315,0.03433952,0.0001775168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867495,0.001753807,0.01052078,0.00003140451,0.0004306405,0.0001612314,0.00004148417,0.0001059073,0.0002052552],"genre_scores_gemma":[0.9942107,0.0002008141,0.003277554,0.0002866775,0.001718627,0.00006060802,0.00008059176,0.00004271845,0.0001216701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03301366,"threshold_uncertainty_score":0.5648153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378604064301806,"score_gpt":0.3335975781187325,"score_spread":0.3198115374757144,"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."}}