{"id":"W2792777789","doi":"10.1016/j.agee.2019.106575","title":"Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database","year":2019,"lang":"en","type":"article","venue":"Agriculture Ecosystems & Environment","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Agriculture and Agri-Food Canada","funders":"FP7 Research for the Benefit of SMEs; AHDB Beef and Lamb; University of California, Davis; National Institute of Food and Agriculture; Rural Development Administration; Beef Cattle Research Council; Department of Agriculture, Food and the Marine, Ireland; Department of Agriculture, Fisheries and Forestry, Australian Government; European Commission; Agence Nationale de la Recherche; Meat and Livestock Australia; Ministerie van Landbouw, Natuur en Voedselkwaliteit; U.S. Department of Agriculture; Scottish Government; Department for Environment, Food and Rural Affairs, UK Government; Commonwealth Scientific and Industrial Research Organisation; Australian Government","keywords":"Forage; Beef cattle; Statistics; Production (economics); Regression; Regression analysis; Mathematics; Covariate; Greenhouse gas; Linear regression; Environmental science; Extant taxon; Econometrics; Animal science; Ecology; Biology; Economics","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":[0.0007528777,0.0004056767,0.0003059621,0.001536825,0.0002348529,0.0004311265,0.0004537693,0.0005013248,0.000587906],"category_scores_gemma":[0.001279806,0.0001716017,0.000547782,0.001220939,0.0001000987,0.000337459,0.0004177709,0.0002119913,0.0001949822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004979866,"about_ca_system_score_gemma":0.0006359618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05032796,"about_ca_topic_score_gemma":0.04782423,"domain_scores_codex":[0.9998429,0.00003703962,0.00001733314,0.00005724484,0.00002568323,0.00001993109],"domain_scores_gemma":[0.9993241,0.0002346028,0.0001518727,0.00006753502,0.0001453644,0.00007662448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005859426,0.0002195014,0.956777,0.00005852805,0.000404662,0.0002641222,0.00008124288,0.02289465,0.002789121,0.00009078299,0.00101066,0.01482387],"study_design_scores_gemma":[0.00004630732,0.0001313034,0.8843528,0.00001833459,0.0001757199,0.0001646869,0.000151742,0.1125089,0.001168592,0.00008600294,0.001181132,0.00001452696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915677,0.0001156101,0.0005760051,0.00003198452,0.000004323275,0.000009143772,0.00753592,0.0000350842,0.0001242182],"genre_scores_gemma":[0.9749722,0.0000967214,0.001918552,0.00001211615,0.000006881844,0.00002137389,0.02276735,0.000005189187,0.0001996238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05032796,"threshold_uncertainty_score":0.1000701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02583401093285732,"score_gpt":0.1988648448518845,"score_spread":0.1730308339190272,"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."}}