{"id":"W4393950572","doi":"10.3390/metabo14040205","title":"Identifying Predictive Biomarkers of Subclinical Mastitis in Dairy Cows through Urinary Metabotyping","year":2024,"lang":"en","type":"article","venue":"Metabolites","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Livestock and Meat Agency; Genome Alberta; University of Alberta","keywords":"Metabolite; Medicine; Subclinical infection; Mastitis; Ice calving; Urine; Urinary system; Physiology; Internal medicine; Animal science; Lactation; Biology; Pregnancy; Pathology","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":[0.0008752209,0.0001766276,0.0003962985,0.00004396382,0.00007862283,0.00007460549,0.0002999926,0.0001290515,0.0008220067],"category_scores_gemma":[0.0003707761,0.00008033008,0.0001910002,0.0009704763,0.0001895804,0.0004567697,0.0001766253,0.0002247592,0.00004064116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001848491,"about_ca_system_score_gemma":0.00002349158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000424685,"about_ca_topic_score_gemma":0.0002729609,"domain_scores_codex":[0.9980853,0.0002810909,0.0005866733,0.0004497965,0.0002758932,0.0003212184],"domain_scores_gemma":[0.9988387,0.0008623242,0.00008207662,0.00009271233,0.00005986877,0.00006432494],"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":[0.0007499267,0.001383395,0.09313647,0.001184108,0.001047917,0.0005590414,0.005978963,0.00006366779,0.4069493,0.1812049,0.006892379,0.3008499],"study_design_scores_gemma":[0.0006121616,0.0004358195,0.6307449,0.001085391,0.0002569221,0.00006492936,0.003166256,0.001065496,0.02311858,0.05046713,0.2881831,0.0007994068],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581273,0.03523155,0.0003322538,0.0007280765,0.001092447,0.0002697005,0.0002839665,0.0001501565,0.00378458],"genre_scores_gemma":[0.9961561,0.001728545,0.001259821,0.0001102346,0.0003064372,0.00003045271,0.00007665201,0.000002332993,0.0003293979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5376084,"threshold_uncertainty_score":0.9000397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0604907016559641,"score_gpt":0.3092022178216218,"score_spread":0.2487115161656577,"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."}}