{"id":"W2963816842","doi":"10.1017/s175173111900154x","title":"Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle","year":2019,"lang":"en","type":"article","venue":"animal","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Research and Innovation; Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs; Agricultural Research Service; Genome Canada; Ontario Ministry of Research, Innovation and Science; Ontario Genomics; Aarhus Universitet; Alberta Innovates - Technology Futures; Ontario Centres of Excellence; Genome Alberta; U.S. Department of Agriculture","keywords":"Dry matter; Animal science; Environmental science; Methane; Feed conversion ratio; Mathematics; Dairy cattle; Trait; Production (economics); Yield (engineering); Total economic value; Biotechnology; Agronomy; Biology; Economics; Body weight; Ecology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000867025,0.0002921807,0.0002649134,0.0004703756,0.0001752882,0.0006271618,0.0005023679,0.0004083358,0.000452166],"category_scores_gemma":[0.00190732,0.0002193749,0.0003452139,0.0004922288,0.0003031456,0.0004016091,0.0003093598,0.0002670983,0.00005324287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002234528,"about_ca_system_score_gemma":0.0008906481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04203808,"about_ca_topic_score_gemma":0.05058239,"domain_scores_codex":[0.9997674,0.00007470699,0.000009246718,0.00005541115,0.00004764373,0.00004549736],"domain_scores_gemma":[0.999464,0.0003843858,0.00008098089,0.00001733855,0.00004137421,0.00001200315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001687465,0.00007410336,0.1034989,0.00005450642,0.0001137588,0.00007515426,0.00003360357,0.8737609,0.00645599,0.001441665,0.00005898763,0.01426371],"study_design_scores_gemma":[0.000009236172,0.00009353708,0.07686591,0.00001350827,0.00003806182,0.00003788326,0.00007627098,0.9183182,0.003089919,0.001173468,0.0002663043,0.00001771056],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751238,0.0001622698,0.02356375,0.00005317271,0.000002534627,0.0000142815,0.0001828329,0.000015508,0.0008817484],"genre_scores_gemma":[0.9945116,0.00008470959,0.004903139,0.000008155144,0.000001337719,0.00000840509,0.0001160506,0.000004238437,0.0003624095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04203808,"threshold_uncertainty_score":0.08358681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006248196428979256,"score_gpt":0.2199902702591243,"score_spread":0.2137420738301451,"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."}}