{"id":"W2005448488","doi":"10.1016/j.compag.2009.12.011","title":"Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems","year":2010,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Stichting voor de Technische Wetenschappen","keywords":"Milking; Mastitis; Interpretation (philosophy); Automatic milking; Computer science; Artificial intelligence; Engineering; Statistics; Medicine; Mathematics; Biology; Pathology; Programming language; Animal science","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":[0.003558946,0.002282308,0.00154137,0.0043024,0.001022605,0.003717565,0.002074467,0.002458282,0.01914049],"category_scores_gemma":[0.04424365,0.00106138,0.0008619977,0.001919588,0.0004232954,0.002508481,0.001913134,0.001762271,0.01683213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007345246,"about_ca_system_score_gemma":0.001023192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004289935,"about_ca_topic_score_gemma":0.004030276,"domain_scores_codex":[0.9959092,0.001593305,0.0007055762,0.0003332972,0.001203198,0.000255437],"domain_scores_gemma":[0.951894,0.02799885,0.00235633,0.008145572,0.00881955,0.0007856983],"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":[0.006374181,0.0005166869,0.01775835,0.001611357,0.0002627656,0.002985378,0.001810423,0.00444609,0.08902513,0.004190145,0.1243854,0.746634],"study_design_scores_gemma":[0.001905887,0.002135524,0.08276761,0.002299273,0.001239339,0.01459877,0.001782398,0.1788847,0.3464321,0.02407899,0.3428805,0.0009949368],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07860233,0.00398542,0.6392983,0.005459067,0.002568302,0.002059742,0.004112329,0.2469902,0.01692436],"genre_scores_gemma":[0.3923463,0.001592395,0.5641757,0.004421046,0.001886683,0.0007248509,0.004013911,0.01044499,0.02039417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01914049,"threshold_uncertainty_score":0.0640313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02091710401119218,"score_gpt":0.2834332871397497,"score_spread":0.2625161831285575,"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."}}