{"id":"W2743321906","doi":"10.2527/asasann.2017.044","title":"044 Serum metabolomics fingerprinting during the dry off period identifies metabolite signatures that can predict the risk of metritis","year":2017,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Metritis; Metabolite; Medicine; Ice calving; Multivariate analysis; Metabolomics; Area under the curve; Postpartum period; Internal medicine; Physiology; Animal science; Pregnancy; Biology; Lactation; Bioinformatics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003274953,0.0001667934,0.000500367,0.0002231888,0.001756971,0.0001852027,0.001072639,0.00003548439,0.0000198554],"category_scores_gemma":[0.005455022,0.00007661877,0.0002635505,0.000355002,0.002167143,0.0005318848,0.0003001315,0.0007080272,6.869665e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005790989,"about_ca_system_score_gemma":0.0002914912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001407595,"about_ca_topic_score_gemma":0.00006540416,"domain_scores_codex":[0.9974243,0.00008192482,0.0004965415,0.000199032,0.001481801,0.0003164247],"domain_scores_gemma":[0.9973109,0.0002939429,0.001238869,0.0004297038,0.00059935,0.0001272101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003741783,0.00008201483,0.6818921,0.000102023,0.0004840153,0.0001088167,0.003897934,0.00006584058,0.3099519,0.0005152168,0.000152428,0.0023735],"study_design_scores_gemma":[0.0006023098,0.0002219031,0.9574463,0.0001837166,0.000406527,0.0002858173,0.001871504,0.0001190343,0.03831691,0.0002324534,0.0002433006,0.00007027656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848932,0.00782538,0.00001113042,0.006037277,0.0005868478,0.0001544387,0.00003879894,0.000008012062,0.0004448962],"genre_scores_gemma":[0.9970907,0.001566701,0.0004258878,0.00008959094,0.0007497895,0.000002162677,1.829871e-7,0.00001024217,0.00006474749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2755542,"threshold_uncertainty_score":0.9995426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01911223741215582,"score_gpt":0.2824738908453075,"score_spread":0.2633616534331517,"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."}}