{"id":"W3013205857","doi":"10.1101/2020.03.25.008276","title":"Self-cutting and integrating CRISPR plasmids (SCIPs) enable targeted genomic integration of genetic payloads for rapid cell engineering","year":2020,"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":"University of Ottawa; National Research Council Canada","funders":"","keywords":"CRISPR; Genome editing; Biology; Plasmid; Genome engineering; Cas9; Computational biology; Gene; Genetics; Insert (composites); Gene targeting","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003717439,0.0004890679,0.0002638751,0.0003719772,0.0001688763,0.000556716,0.0003478958,0.0004908323,0.001247362],"category_scores_gemma":[0.0002713302,0.0001944993,0.0003881039,0.0002103896,0.0003649697,0.0002965621,0.0003979632,0.0008686694,0.000693501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000283859,"about_ca_system_score_gemma":0.0002964024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001695405,"about_ca_topic_score_gemma":0.0002560313,"domain_scores_codex":[0.9996521,0.00005368948,0.00003252285,0.0000559931,0.0001515768,0.00005399763],"domain_scores_gemma":[0.9997343,0.00005204355,0.00009212302,0.00005038056,0.00003050628,0.00004055626],"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":[0.00001272064,0.000012446,0.00007088881,0.00003254744,0.000004182905,0.00005289874,0.000006135333,0.0001118408,0.9983037,0.0002022867,0.00004450401,0.001145929],"study_design_scores_gemma":[0.000002838121,0.00006516314,0.0002822051,0.000002431895,0.00000555993,0.0001897762,0.000005402293,0.0007270025,0.996568,0.00003924382,0.002109691,0.000002703073],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.807508,0.002837595,0.1799391,0.0003221301,0.0001675595,0.0004345433,0.001224892,0.00189473,0.005671445],"genre_scores_gemma":[0.9372864,0.001543497,0.05427502,0.0001157118,0.00002479436,0.0001660834,0.0009673253,0.0001160188,0.005505076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001247362,"threshold_uncertainty_score":0.004172862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006642081482396149,"score_gpt":0.2192027621151672,"score_spread":0.212560680632771,"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."}}