{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007163959,0.0003899251,0.0005338332,0.001909776,0.0003977525,0.001351183,0.0002940227,0.0006817813,0.001610293],"category_scores_gemma":[0.001212933,0.0002323363,0.0004796249,0.0018721,0.0005389687,0.0006607761,0.001388624,0.0005461422,0.0005660508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003523353,"about_ca_system_score_gemma":0.0002819021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001138785,"about_ca_topic_score_gemma":0.001146979,"domain_scores_codex":[0.9992009,0.0001124469,0.00005407449,0.0003504858,0.0001405692,0.0001415429],"domain_scores_gemma":[0.9990247,0.000176941,0.0004914704,0.0001009435,0.00008796607,0.0001178848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004674943,0.0001485786,0.5403763,0.0009543557,0.0007190563,0.0003746607,0.0007778334,0.004193303,0.4023558,0.001837072,0.001538553,0.04625697],"study_design_scores_gemma":[0.00001298179,0.0001962413,0.9725179,0.00008459926,0.0001381227,0.0005073231,0.0006001146,0.006475777,0.01037894,0.001969071,0.00707575,0.00004312723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862871,0.002527277,0.002663448,0.0001868216,0.00001665512,0.00001906055,0.007035954,0.0001667091,0.001096983],"genre_scores_gemma":[0.9863175,0.001001271,0.004571452,0.0001980844,0.00003981709,0.00003988291,0.00727501,0.00008868812,0.0004682188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001909776,"threshold_uncertainty_score":0.005386949,"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."}}