{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001383705,0.0001573482,0.0002504546,0.000013191,0.0004901156,0.00003629048,0.0002194431,0.0001256376,0.00002900996],"category_scores_gemma":[0.000005331401,0.00006411353,0.00003407411,0.00002824733,0.0006184161,0.00005354336,0.000161486,0.0001474508,0.00001590612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001893458,"about_ca_system_score_gemma":0.000001879399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007298205,"about_ca_topic_score_gemma":0.00001798056,"domain_scores_codex":[0.9991047,0.00005743864,0.0001692769,0.0003661099,0.00002139622,0.0002810551],"domain_scores_gemma":[0.9996374,0.0001294994,0.0001034532,0.00008025586,0.000001969423,0.00004739228],"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":[0.00001451477,0.00004369238,0.03422416,0.00000406362,0.00001096525,6.836849e-7,0.00007410005,1.521817e-7,0.9597479,0.0001241571,0.00001666041,0.005738965],"study_design_scores_gemma":[0.0003700416,0.000104551,0.896198,0.000006028019,0.0000144431,0.00001317206,0.0004174099,7.279291e-7,0.1006231,0.0007861624,0.001318512,0.0001478277],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976127,0.0003599394,0.000001141383,0.001109875,0.00003772522,0.0003301181,0.0002552181,0.00001005252,0.0002832856],"genre_scores_gemma":[0.9990836,0.0003256933,0.00003319717,0.0002072837,0.00006212984,0.00002769371,0.0002068821,0.000001534015,0.00005196109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8619738,"threshold_uncertainty_score":0.3769622,"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."}}