{"id":"W3033323769","doi":"10.3168/jds.2019-17758","title":"Short communication: Potential prediction of vitamin B12 concentration based on mid-infrared spectral data using Holstein Dairy Herd Improvement milk samples","year":2020,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ste. Anne's Hospital; Université Laval; Cégep de Sherbrooke; Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; Canadian Dairy Commission; Ministère de la Santé; Ministère de la Santé et des Services sociaux; Dairy Farmers of Canada; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Université Laval","keywords":"Herd; Vitamin B12; Mathematics; Cutoff; Statistics; Mahalanobis distance; Linear discriminant analysis; Outlier; Animal science; Chemistry; Biology; Physics","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":[0.0006049424,0.0001425519,0.0002802465,0.0001338526,0.0002130596,0.00009804365,0.001311028,0.00006434977,0.0001916357],"category_scores_gemma":[0.0003326131,0.0001294662,0.00009625743,0.0008920401,0.0003451134,0.0008309844,0.000156849,0.0002987826,8.25892e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057909,"about_ca_system_score_gemma":0.0004561695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001498694,"about_ca_topic_score_gemma":0.000001060842,"domain_scores_codex":[0.9977645,0.00003051697,0.0006925687,0.0002970631,0.0009832691,0.0002320917],"domain_scores_gemma":[0.998271,0.00007917286,0.0005673377,0.0006598287,0.0002391171,0.0001835699],"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":[0.0001522125,0.0001898315,0.004783455,0.00004774176,0.00004084511,0.000007361483,0.0002514682,0.002812913,0.9905688,0.00000820409,0.0004997498,0.0006374847],"study_design_scores_gemma":[0.000786789,0.0003362517,0.003572244,0.00009375567,0.0001828959,0.00001800565,0.001430648,0.09482399,0.8985034,0.00002571817,0.0001050499,0.000121227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850795,0.0002158987,0.01321034,0.0005614763,0.0001648929,0.00006843574,0.00014252,0.00001794914,0.000539026],"genre_scores_gemma":[0.9916056,0.0000484403,0.007872899,0.0001551159,0.0002397711,5.921107e-7,0.00005138332,0.000008420096,0.00001778101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09206531,"threshold_uncertainty_score":0.527948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06581730340275578,"score_gpt":0.3079510441855164,"score_spread":0.2421337407827606,"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."}}