{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008471112,0.0001047677,0.0001686227,0.00002534997,0.0004201942,0.0001028684,0.002358517,0.00007079759,0.0002059648],"category_scores_gemma":[0.0002441369,0.00005629298,0.00004058065,0.0003975305,0.0001004845,0.0002320125,0.001645976,0.0001942188,0.000031299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004882921,"about_ca_system_score_gemma":0.00001718592,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006423038,"about_ca_topic_score_gemma":0.06181466,"domain_scores_codex":[0.9984479,0.0005254413,0.0003875066,0.0002864297,0.0001666287,0.0001860896],"domain_scores_gemma":[0.9982433,0.0006184294,0.00009093678,0.0009605766,0.00004789855,0.00003885465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004723575,0.001190334,0.2809809,0.00003617495,0.0001679679,0.000003508101,0.001897557,0.00002225114,0.1692861,0.0118915,0.01611785,0.5183586],"study_design_scores_gemma":[0.0003880492,0.00002273113,0.5459225,0.0004405023,0.00004666941,5.236099e-7,0.001713789,0.002950938,0.001630616,0.00946987,0.4370851,0.0003287295],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94231,0.007284569,0.001151292,0.02390107,0.0009387411,0.0006751579,0.0008462075,0.000215956,0.02267702],"genre_scores_gemma":[0.9916334,0.000955018,0.004819201,0.0005171432,0.00005476139,0.00004444471,0.001663329,8.457442e-7,0.0003118296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5180299,"threshold_uncertainty_score":0.9709755,"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."}}