{"id":"W4413583452","doi":"10.1016/j.anifeedsci.2025.116483","title":"Assessing the methane-mitigating effects of feed additives on dairy cows: Validation of an AI-Based predictive model using a monensin feed additive","year":2025,"lang":"en","type":"article","venue":"Animal Feed Science and Technology","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Monensin; Feed additive; Animal science; Dairy cattle; Dry matter; Methane emissions; Food science; Chemistry; Methane; Biotechnology; Biology; Broiler","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004387839,0.0001496575,0.0002883016,0.0001777259,0.0004430211,0.00004179544,0.0003065133,0.000149971,0.000004403839],"category_scores_gemma":[0.000934847,0.00006687299,0.00004269461,0.001787537,0.002140037,0.0004103386,0.0001127335,0.0001880624,3.558524e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003845085,"about_ca_system_score_gemma":0.0001259572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007645445,"about_ca_topic_score_gemma":0.00001041741,"domain_scores_codex":[0.9986627,0.0001386431,0.0002505154,0.0004314069,0.0002442402,0.0002724758],"domain_scores_gemma":[0.9983833,0.0007238121,0.0002288304,0.00009183797,0.0005289874,0.00004327146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008705824,0.0001282421,0.0006173534,0.00001493704,0.000009089177,0.000001442035,0.0000659701,0.00009836338,0.9879729,0.003488833,0.0000112222,0.00750464],"study_design_scores_gemma":[0.0003029327,0.001191386,0.05760248,0.0002300288,0.00002779271,0.000002521273,0.001978457,0.04180412,0.8905377,0.006210713,0.000006495315,0.0001053986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976708,0.00004834291,0.0001633432,0.001341825,0.0000390748,0.0003360283,0.00005102577,0.00005511834,0.0002944393],"genre_scores_gemma":[0.9991772,0.000007595433,0.0004950792,0.0002558526,0.00001748294,0.00002179014,0.00001922965,0.000001122572,0.000004603103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09743518,"threshold_uncertainty_score":0.7885054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160802298428055,"score_gpt":0.29278732168459,"score_spread":0.2711792987003095,"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."}}