{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005308059,0.0002703675,0.0003254433,0.0006553552,0.0001432757,0.0004646217,0.0001321997,0.0003201316,0.0002639815],"category_scores_gemma":[0.0005382159,0.0001353837,0.000176746,0.0004685056,0.0001231696,0.000142829,0.0002300331,0.0001830616,0.00006820796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350722,"about_ca_system_score_gemma":0.0001492096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008200309,"about_ca_topic_score_gemma":0.001255504,"domain_scores_codex":[0.9998056,0.00005198634,0.0000144834,0.0000522883,0.0000445552,0.00003100823],"domain_scores_gemma":[0.9996871,0.00005820693,0.0001708987,0.00001783511,0.00003699896,0.00002903974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001623081,0.0001261353,0.728461,0.0001105224,0.0001666675,0.0001813676,0.0001272175,0.0004144516,0.2403707,0.00005256412,0.0001436047,0.02822269],"study_design_scores_gemma":[0.00001222315,0.0006755438,0.9817686,0.00001427588,0.0001047276,0.0003981691,0.0001173156,0.002600304,0.01371198,0.00008355523,0.0005036995,0.000009590418],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969976,0.001252358,0.001241934,0.00002920589,0.000004247371,0.00001013423,0.00030147,0.00001849427,0.0001444982],"genre_scores_gemma":[0.9968674,0.0003636137,0.002225545,0.00003734852,0.000006381492,0.00001083743,0.0003638132,0.000003087931,0.0001220141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008200309,"threshold_uncertainty_score":0.0028072,"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."}}