{"id":"W3168435431","doi":"10.1101/2021.06.08.447622","title":"An Efficient and Cost-effective Purification Methodology for SaCas9 Nuclease","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Ted Rogers Centre for Heart Research; University of Toronto","funders":"University of Toronto","keywords":"Nuclease; Cas9; Simplicity; Scalability; CRISPR; Computer science; Biochemical engineering; Chemistry; Process engineering; Engineering; Biochemistry; DNA; Database","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":[0.0008486394,0.0008532766,0.0004351688,0.000711063,0.0006049777,0.0005040803,0.0007226305,0.0007081244,0.002387947],"category_scores_gemma":[0.000975686,0.0003998053,0.0004643151,0.000576215,0.0003761343,0.0006435142,0.0005727963,0.001974484,0.004417447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004016547,"about_ca_system_score_gemma":0.0008138725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005085231,"about_ca_topic_score_gemma":0.0008973484,"domain_scores_codex":[0.9991444,0.0001241817,0.000100793,0.0002111895,0.0003692569,0.00005015922],"domain_scores_gemma":[0.9995912,0.00007443036,0.00007242052,0.0001056241,0.000114402,0.00004195009],"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":[0.00002376115,0.00003417458,0.0001059754,0.0001524453,0.00001356978,0.00009722895,0.00002592541,0.0001084592,0.9877377,0.0006488013,0.0009656229,0.01008641],"study_design_scores_gemma":[0.00001021184,0.00005072277,0.0006113519,0.00001390423,0.00001556711,0.0005806616,0.00001155055,0.000818585,0.9636726,0.0004679256,0.033725,0.00002185768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06761128,0.004200665,0.9156173,0.00103175,0.001050116,0.0006464953,0.002399179,0.002938459,0.004504688],"genre_scores_gemma":[0.1484,0.004805879,0.8221467,0.0004803399,0.000218279,0.0007162453,0.009382428,0.0006092763,0.01324081],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002387947,"threshold_uncertainty_score":0.007988453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987787064879245,"score_gpt":0.3034994710632382,"score_spread":0.2836216004144458,"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."}}