{"id":"W3035259135","doi":"10.1101/2020.06.15.151498","title":"Mobile element warfare via CRISPR and anti-CRISPR in <i>Pseudomonas aeruginosa</i>","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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; University of California, San Francisco; University of Toronto; Montana State University; Sandler Foundation; Innovative Genomics Institute; Vallee Foundation; Howard Hughes Medical Institute","keywords":"CRISPR; Mobile genetic elements; Trans-activating crRNA; Pseudomonas aeruginosa; Biology; Mutagenesis; Transposable element; Genetics; DNA; Computational biology; Genome; Cas9; Bacteria; Gene; Mutation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000177689,0.0003203566,0.0002232413,0.0001808223,0.0002142995,0.0005058204,0.0003207628,0.0003974712,0.001175108],"category_scores_gemma":[0.0001980682,0.0001051713,0.0002965717,0.0001030021,0.0002797676,0.0002617108,0.0003347353,0.0005566404,0.0003632017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005910078,"about_ca_system_score_gemma":0.0003663955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001437811,"about_ca_topic_score_gemma":0.001786206,"domain_scores_codex":[0.9997217,0.00003503387,0.00001751796,0.00006261613,0.0001035475,0.00005954156],"domain_scores_gemma":[0.9998885,0.00001641308,0.00003907338,0.00001128061,0.00001828277,0.00002645931],"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":[0.00007763151,0.00003524136,0.001116469,0.00005565694,0.00000946821,0.0001007455,0.00001814539,0.000366136,0.9908158,0.0004637869,0.0001895064,0.00675151],"study_design_scores_gemma":[0.00002779558,0.0007364285,0.008835711,0.00001596805,0.00003429483,0.001064769,0.0000947326,0.006119495,0.9717318,0.0004181887,0.01089619,0.00002466882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765888,0.002636444,0.01366725,0.0004357253,0.0000768865,0.00008229601,0.0002431799,0.0003420871,0.005927455],"genre_scores_gemma":[0.9900268,0.0006628775,0.005965122,0.0001498478,0.00001482737,0.00003286222,0.0002882342,0.00002713452,0.002832275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001437811,"threshold_uncertainty_score":0.004288077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007934127064310948,"score_gpt":0.2467486351841449,"score_spread":0.238814508119834,"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."}}