{"id":"W4417290406","doi":"10.3168/jdsc.2025-0825","title":"Identifying data anomalies in milk component measurements from partial-day milking records","year":2025,"lang":"en","type":"article","venue":"JDS Communications","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Univariate; Component (thermodynamics); Metric (unit); Consistency (knowledge bases); Multivariate statistics; Reliability (semiconductor); Milking; Data quality","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002554543,0.0004191576,0.0004864193,0.001519745,0.0003132373,0.0007233511,0.0005159791,0.0004293277,0.0003878923],"category_scores_gemma":[0.01225969,0.0001305977,0.000365953,0.001831875,0.0003162755,0.0004937921,0.0006931508,0.0004252596,0.0001901965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003451848,"about_ca_system_score_gemma":0.0007629207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003484759,"about_ca_topic_score_gemma":0.008453541,"domain_scores_codex":[0.9981427,0.0005437912,0.0001732686,0.0003971168,0.0006349928,0.0001080996],"domain_scores_gemma":[0.992058,0.003533021,0.002080007,0.001023793,0.001152975,0.000152305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005754129,0.0001106032,0.7140039,0.0002374855,0.0003599816,0.000209621,0.0004962127,0.0255011,0.03247023,0.0009552768,0.001088816,0.2239914],"study_design_scores_gemma":[0.00001530771,0.0002886481,0.7136548,0.00004834717,0.0001038312,0.0005767379,0.0003726849,0.25376,0.02684474,0.001628111,0.002629024,0.00007772593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8720397,0.0004115915,0.1246875,0.0000858944,0.00004460611,0.00006623279,0.001133216,0.0006442216,0.0008870008],"genre_scores_gemma":[0.9505982,0.000102242,0.04760264,0.00002246911,0.00001896902,0.00003455159,0.001286758,0.00004409357,0.0002900981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003484759,"threshold_uncertainty_score":0.01350981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3063427972866078,"score_gpt":0.3633414906215314,"score_spread":0.05699869333492363,"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."}}