{"id":"W2551120435","doi":"10.3389/fmicb.2016.01820","title":"The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism","year":2016,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Product Board Animal Feed; Canada Research Chairs","keywords":"Rumen; Fermentation; Digestion (alchemy); Biology; Metabolism; Forage; Starch; Dry matter; Biochemistry; Food science; Animal science; Chemistry; Agronomy; Chromatography","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":[0.0002430536,0.00007572211,0.0002571958,0.000025358,0.00005643025,0.000003098054,0.0001702912,0.00008310634,0.00005352968],"category_scores_gemma":[0.0001153399,0.00002269918,0.00005759291,0.0001480298,0.0001721781,0.0000287931,0.00004993734,0.00004172985,0.000009305289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005938131,"about_ca_system_score_gemma":0.000007012168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008222692,"about_ca_topic_score_gemma":0.00005418974,"domain_scores_codex":[0.9992162,0.0001156704,0.0002678386,0.00015027,0.00002985578,0.0002201542],"domain_scores_gemma":[0.9995793,0.0002195828,0.00008422683,0.00004617564,0.00004170702,0.00002897492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001328761,0.0000375145,0.0002885158,0.000002263548,0.00001163289,3.184906e-7,0.00001797811,0.000007101057,0.9547247,0.0419728,0.0001988361,0.002605439],"study_design_scores_gemma":[0.001719267,0.0009954243,0.01271445,0.0002438242,0.00003776783,0.00002305001,0.0017536,0.001741791,0.1209302,0.8521596,0.007230562,0.0004504998],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750579,0.0004110594,0.02022791,0.003402866,0.0002446539,0.000240338,0.00005874228,0.00001065757,0.0003458544],"genre_scores_gemma":[0.9994751,0.0001040661,0.0003162668,0.00003204144,0.00001512945,0.000007294546,0.00001089233,6.085282e-7,0.00003866464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8337945,"threshold_uncertainty_score":0.09256457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697394905380205,"score_gpt":0.225301154054978,"score_spread":0.208327205001176,"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."}}