{"id":"W2953230159","doi":"10.1101/053058","title":"Continuous Genetic Recording with Self-Targeting CRISPR-Cas in Human Cells","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; National Institutes of Health; National Science Foundation","keywords":"Biology; CRISPR; Cas9; Computational biology; DNA; Cell biology; Population; Genetics; Gene; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004236429,0.0006871677,0.0005839211,0.0002261512,0.0001259452,0.0001275219,0.0005581147,0.0006405598,0.00002188093],"category_scores_gemma":[0.00006508599,0.0006563559,0.0001526268,0.0002079953,0.00007443316,0.000008036199,0.0005224131,0.0005177987,0.00001730477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001378622,"about_ca_system_score_gemma":0.0002483503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005271469,"about_ca_topic_score_gemma":0.00001056834,"domain_scores_codex":[0.996892,0.0001071033,0.0006222656,0.001295193,0.000264121,0.0008192972],"domain_scores_gemma":[0.9980239,0.00002302108,0.0003031691,0.001151445,0.0002565291,0.0002419074],"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":[0.00002826198,0.00007566666,0.01687355,0.0002402469,0.0001293831,0.00007459425,0.00001048424,0.0004429755,0.9817093,0.00001663986,0.0003947613,0.000004127707],"study_design_scores_gemma":[0.000874576,0.0001696493,0.01903907,0.0004345333,0.0000787804,8.222064e-8,0.000005320702,0.0001068608,0.9738224,0.000001838316,0.004438029,0.001028914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768735,0.002163358,0.01925479,0.00005788797,0.0007005161,0.0006887666,0.00005842344,0.0001731456,0.00002965081],"genre_scores_gemma":[0.9869366,0.0004013039,0.01132343,0.00006791094,0.0008663475,0.0001687035,8.298618e-7,0.0002192014,0.0000157138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0100631,"threshold_uncertainty_score":0.9995888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005271597011996058,"score_gpt":0.2311468970891448,"score_spread":0.2258753000771488,"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."}}