{"id":"W2772826412","doi":"10.3389/fmicb.2017.02445","title":"Enhancing the Resolution of Rumen Microbial Classification from Metatranscriptomic Data Using Kraken and Mothur","year":2017,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Alberta","funders":"Empresa Brasileira de Pesquisa Agropecuária; Natural Sciences and Engineering Research Council of Canada; Alberta Livestock and Meat Agency; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Biology; Bacteroidetes; Proteobacteria; Firmicutes; Archaea; Phylum; Microbiome; Metagenomics; Context (archaeology); Rumen; 16S ribosomal RNA; Zoology; Evolutionary biology; Genetics; Bacteria; Gene; Biochemistry","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":[0.007905889,0.002458128,0.001811474,0.004464237,0.001200265,0.003661808,0.001096217,0.001294343,0.001618819],"category_scores_gemma":[0.01137279,0.001217169,0.004307344,0.002059846,0.0005735661,0.002091182,0.002727047,0.002489492,0.001988235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005884183,"about_ca_system_score_gemma":0.001460503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002580443,"about_ca_topic_score_gemma":0.005502191,"domain_scores_codex":[0.9975286,0.0007053304,0.0002755969,0.0008450015,0.0004157526,0.0002296871],"domain_scores_gemma":[0.9974337,0.001265154,0.0004288419,0.0003532838,0.0004094749,0.0001094522],"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":[0.004655624,0.0006433602,0.07094508,0.004231368,0.003201407,0.000950551,0.003022064,0.01783943,0.5411159,0.003305668,0.005664394,0.3444251],"study_design_scores_gemma":[0.0003708327,0.001073224,0.1161739,0.0005682592,0.001312757,0.001585054,0.002302148,0.5834723,0.2543184,0.01042342,0.02768468,0.0007151375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3982008,0.002530016,0.566999,0.000449315,0.0002245109,0.0006887719,0.01289046,0.01563585,0.002381374],"genre_scores_gemma":[0.1880197,0.0005996393,0.7996504,0.0002432871,0.00004066708,0.0005727481,0.00935918,0.001110428,0.0004039591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007905889,"threshold_uncertainty_score":0.04181081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06026916871625928,"score_gpt":0.2636502068523616,"score_spread":0.2033810381361023,"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."}}