{"id":"W205389130","doi":"","title":"MINING SENSOR DATA TO DISCOVER CLINICAL MASTITIS","year":2011,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Stichting voor de Technische Wetenschappen; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Automatic milking; Decision tree; Quarter (Canadian coin); Mastitis; Milking; Data mining; Sensitivity (control systems); Computer science; Medicine; Engineering; Geography; Forestry; Electronic engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001683813,0.0005826417,0.0006507734,0.001069405,0.0001894143,0.0005318923,0.0005845827,0.0005547738,0.0004877469],"category_scores_gemma":[0.004478916,0.0002657388,0.0006976421,0.0008126124,0.0001868044,0.0004799697,0.0002683395,0.0003305136,0.0001367086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005231268,"about_ca_system_score_gemma":0.0005345622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005999033,"about_ca_topic_score_gemma":0.00587866,"domain_scores_codex":[0.9994923,0.0001531689,0.00007243622,0.0001357769,0.00009026214,0.00005597758],"domain_scores_gemma":[0.9974077,0.001805034,0.0003323563,0.0001280514,0.000262842,0.00006399292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007673288,0.0006254754,0.4270137,0.0002801589,0.0003892002,0.0004760263,0.0001957392,0.4305793,0.007117253,0.0007251488,0.00151929,0.1303113],"study_design_scores_gemma":[0.0000220593,0.0001468126,0.04463347,0.00001986056,0.00005419376,0.0001140804,0.00007110067,0.9505997,0.002562535,0.001267656,0.0004957195,0.00001280965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9227615,0.0004202515,0.07283436,0.0003531245,0.0000282112,0.0001332627,0.002380565,0.0003541971,0.0007345828],"genre_scores_gemma":[0.9815902,0.000111321,0.01583527,0.00002588137,0.000008801379,0.00005305259,0.002130742,0.000006138882,0.0002386951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005999033,"threshold_uncertainty_score":0.0119282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2113768673236771,"score_gpt":0.3181204274761599,"score_spread":0.1067435601524828,"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."}}