{"id":"W2190464249","doi":"10.3168/jds.2015-9619","title":"Effects of replacing grass silage with forage pearl millet silage on milk yield, nutrient digestion, and ruminal fermentation of lactating dairy cows","year":2015,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Silage; Dry matter; Latin square; Neutral Detergent Fiber; Forage; Nutrient; Digestion (alchemy); Biology; Animal science; Dairy cattle; Rumen; Agronomy; Fermentation; Chemistry; Food science","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":[0.0004419572,0.0004371942,0.0003835199,0.0002201324,0.0002780752,0.0004480155,0.0003015294,0.0003469277,0.0005771539],"category_scores_gemma":[0.0007529599,0.0002581979,0.0002505434,0.0001703537,0.000308316,0.0004089269,0.0002314171,0.0004140471,0.0001239902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000457854,"about_ca_system_score_gemma":0.000338482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00215887,"about_ca_topic_score_gemma":0.004696515,"domain_scores_codex":[0.999666,0.0001003873,0.00003527067,0.00007298521,0.00006789774,0.0000575023],"domain_scores_gemma":[0.9993573,0.0001409233,0.0002029739,0.00003279778,0.00005055192,0.0002155425],"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":[0.0271819,0.002170958,0.02257365,0.0002387832,0.0003107713,0.000251732,0.0002692653,0.0004103042,0.9296985,0.00003022174,0.00007565021,0.0167883],"study_design_scores_gemma":[0.001170027,0.09481516,0.4985453,0.00007124814,0.0009642066,0.0008215617,0.001101823,0.003914713,0.3947521,0.0001396603,0.003622025,0.00008218038],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999718,0.0001586827,0.00004245515,0.000008622294,0.00000294657,0.000003180567,0.00001373018,0.000002609851,0.00004977926],"genre_scores_gemma":[0.9988315,0.0001929097,0.0005381876,0.00008506999,0.000006937787,0.000008578624,0.00008948429,0.000005200014,0.0002422809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00215887,"threshold_uncertainty_score":0.004292607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054826035811837,"score_gpt":0.24596745409754,"score_spread":0.2254191937394217,"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."}}