{"id":"W3149158769","doi":"10.1128/mbio.03338-20","title":"Cooperation between Different CRISPR-Cas Types Enables Adaptation in an RNA-Targeting System","year":2021,"lang":"en","type":"article","venue":"mBio","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Koneen Säätiö; Government of Canada; Jane ja Aatos Erkon Säätiö; Academy of Finland; Natural Sciences and Engineering Research Council of Canada; European Commission; U.S. Department of Agriculture","keywords":"CRISPR; Adaptation (eye); RNA; Computational biology; Computer science; Biology; Genetics; Gene; Neuroscience","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":[0.00007525634,0.00008523441,0.00009549468,0.0000231024,0.00003816347,0.00003217385,0.00005032148,0.00007994103,0.000008166972],"category_scores_gemma":[0.00003085333,0.00008475505,0.00002464447,0.00005579303,0.000006782799,0.000003667132,0.00003038227,0.00004764816,0.000003920921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001995694,"about_ca_system_score_gemma":0.00002403182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003106465,"about_ca_topic_score_gemma":0.0002799697,"domain_scores_codex":[0.9994197,0.00004363915,0.0001486794,0.0001996255,0.00005867207,0.0001296266],"domain_scores_gemma":[0.999739,0.000006234598,0.00002080184,0.0001362147,0.0000584014,0.00003935062],"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":[0.000008335472,0.00002525871,0.01690267,0.00005612418,0.00001607297,0.000006655706,0.0002022638,0.004461148,0.9747878,0.00007624665,0.0001195091,0.003337863],"study_design_scores_gemma":[0.0002553495,0.00006945007,0.02153257,0.00002368306,0.00001305186,0.000002880184,0.0005458671,0.003680722,0.9724417,0.000004815815,0.001290523,0.000139412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606006,0.0009922405,0.03798041,0.00004427237,0.0001197627,0.00006493615,0.000007167443,0.00001497405,0.0001756237],"genre_scores_gemma":[0.9985461,0.00003422042,0.0006500991,0.00002405954,0.0002258933,0.000009138169,0.000385376,0.00001301948,0.0001121561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03794543,"threshold_uncertainty_score":0.3456211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419149672795512,"score_gpt":0.2870304109158798,"score_spread":0.2728389141879247,"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."}}