{"id":"W3014495926","doi":"10.1101/2020.04.01.020958","title":"Ecology and molecular targets of hypermutation in the global microbiome","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of British Columbia","funders":"Lawrence Berkeley National Laboratory; California NanoSystems Institute; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; Office of Science; Genome British Columbia; National Energy Research Scientific Computing Center; Australian Research Council; Genome Canada; Joint Genome Institute; National Science Foundation; Compute Canada; University of California, Santa Barbara; U.S. Department of Energy","keywords":"Metagenomics; Biology; Genome; Evolutionary biology; Genetics; Niche; Gene; Organism; Bacterial genome size; Microbiome; Computational biology; Somatic hypermutation; Ecology","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":[0.000221923,0.0002527578,0.0002973885,0.00004173881,0.00003980969,0.00002719232,0.0003190273,0.0002941395,0.000001229477],"category_scores_gemma":[0.00008876489,0.0002342448,0.00006671636,0.0001467567,0.0001360386,7.555022e-7,0.0004434975,0.00015677,0.000001873284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002844753,"about_ca_system_score_gemma":0.0001697206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002754333,"about_ca_topic_score_gemma":0.000008973922,"domain_scores_codex":[0.998739,0.0001210288,0.0002936933,0.0005301065,0.00009166323,0.0002245286],"domain_scores_gemma":[0.9991933,0.00001290464,0.0001849686,0.0004294653,0.0001233519,0.00005603486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002312731,0.00004141114,0.02416133,0.0001086586,0.00009892866,0.00001468147,0.00001200643,0.00004768238,0.9751176,0.0003159186,0.00005770826,9.761808e-7],"study_design_scores_gemma":[0.0005321174,0.0001853319,0.506523,0.00003211715,0.00008368702,8.655429e-8,0.00001065226,0.00003714147,0.4894751,0.00003916603,0.00270716,0.0003743718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932875,0.00512119,0.0002017778,0.0005528458,0.0002027419,0.0003810199,0.0002383981,0.000004905188,0.000009631711],"genre_scores_gemma":[0.9970853,0.0005379952,0.001725231,0.0004735382,0.00009630928,0.00005597965,0.000001206238,0.0000242755,2.131819e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4856424,"threshold_uncertainty_score":0.9552227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00837982090735719,"score_gpt":0.2119689716474634,"score_spread":0.2035891507401062,"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."}}