{"id":"W2591278429","doi":"10.1128/aem.00061-17","title":"Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle","year":2017,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":359,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures; Natural Sciences and Engineering Research Council of Canada; Alberta Livestock and Meat Agency; Government of Canada","keywords":"Biology; Lachnospiraceae; Rumen; Firmicutes; Microbiome; Ruminococcus; Bacteroidetes; Metagenomics; Proteobacteria; Euryarchaeota; Bacterial phyla; Microbiology; Beef cattle; 16S ribosomal RNA; Food science; Bacteria; Biochemistry; Animal science; Fermentation; Genetics; Gene","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":[0.0004740696,0.0003206591,0.0003554997,0.0009481026,0.0002599008,0.0006579041,0.000132703,0.0003051842,0.0003638439],"category_scores_gemma":[0.0003459837,0.0002371294,0.0002483522,0.0004954587,0.0002232826,0.0002378828,0.0002409978,0.0002704789,0.00007424597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002383989,"about_ca_system_score_gemma":0.0001745683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002838952,"about_ca_topic_score_gemma":0.003438645,"domain_scores_codex":[0.9997112,0.00006087646,0.00001653345,0.00007586928,0.0000675528,0.00006782521],"domain_scores_gemma":[0.9997173,0.00005441145,0.0001028273,0.00001343412,0.00005878681,0.00005328591],"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":[0.0007953998,0.00006181791,0.1945953,0.00009218909,0.0001683128,0.00006138131,0.0002505534,0.0001808671,0.7967384,0.00003516623,0.00003434804,0.00698626],"study_design_scores_gemma":[0.00000592514,0.0002777052,0.9750622,0.00001308133,0.00008825507,0.0001365163,0.0003562996,0.001084899,0.02257939,0.00005187422,0.0003348071,0.000009216618],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999043,0.0003077034,0.0003835095,0.000008283916,0.000001516041,0.000002799713,0.0001832303,0.000002223614,0.0000676919],"genre_scores_gemma":[0.9985877,0.0001907299,0.0006430936,0.00002585155,0.000004609875,0.00000741511,0.0003541973,0.000002631133,0.000183654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002838952,"threshold_uncertainty_score":0.005644917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286870580730646,"score_gpt":0.2080274396097305,"score_spread":0.195158733802424,"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."}}