{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002784943,0.00009683417,0.0002337599,0.0000167732,0.0002714788,0.00002842586,0.0006042898,0.0001225219,0.00002303247],"category_scores_gemma":[0.00006715793,0.00004084018,0.00003116177,0.00004602597,0.0004520904,0.0001497195,0.0001746286,0.0001072396,0.000001781018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002127547,"about_ca_system_score_gemma":0.00001027728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110484,"about_ca_topic_score_gemma":0.0009502238,"domain_scores_codex":[0.9990435,0.0001758118,0.0002425712,0.000331932,0.00002213369,0.0001840389],"domain_scores_gemma":[0.9994368,0.00007728028,0.0002165697,0.0002212926,0.00002697956,0.00002109615],"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":[0.00007336969,0.00002634096,0.01266404,0.000003129817,0.00001457737,4.354147e-7,0.00005377286,6.420181e-7,0.982344,0.00004313961,0.001019385,0.003757151],"study_design_scores_gemma":[0.0006883633,0.0001114701,0.8707709,0.00005533572,0.00005520933,0.0000131258,0.0009291161,0.001254273,0.1092221,0.002209638,0.01445221,0.0002382831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963782,0.0009418489,0.0002973863,0.001152792,0.0004464941,0.0001623157,0.0005690549,0.000007134819,0.00004472028],"genre_scores_gemma":[0.996671,0.0002595801,0.002398344,0.00009077282,0.0001042058,0.000002782353,0.0004572542,9.393414e-7,0.00001509405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8731219,"threshold_uncertainty_score":0.2088023,"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."}}