{"id":"W3208959645","doi":"10.1016/j.prevetmed.2021.105527","title":"Calculating clinical mastitis frequency in dairy cows: Incidence risk at cow level, incidence rate at cow level, and incidence rate at quarter level","year":2021,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Incidence (geometry); Quarter (Canadian coin); Mastitis; Medicine; Rate ratio; Population; Demography; Animal science; Veterinary medicine; Mathematics; Environmental health; Biology; Geography; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004738177,0.0005935007,0.001068523,0.00006183178,0.000805406,0.00005980581,0.0006203891,0.0003632297,0.003153154],"category_scores_gemma":[0.004268077,0.0003323262,0.000195906,0.0007133179,0.001128102,0.0006508845,0.001972716,0.0007235949,0.0001353035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004194849,"about_ca_system_score_gemma":0.00008769392,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00253879,"about_ca_topic_score_gemma":0.02927689,"domain_scores_codex":[0.991787,0.003498887,0.001635239,0.001546301,0.0006765749,0.0008559702],"domain_scores_gemma":[0.9942595,0.003840355,0.0007610926,0.0003767533,0.0003034672,0.0004587971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005501717,0.0002174784,0.692257,0.0002007837,0.00008695706,0.002885633,0.001656244,0.00001790184,0.2799622,0.0001276348,0.00256126,0.01947677],"study_design_scores_gemma":[0.001466008,0.001993724,0.9858338,0.001632574,0.00007838709,0.0007984906,0.0009873384,0.0003831649,0.001241952,0.001392681,0.003516666,0.0006752531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921449,0.002710352,0.0004633957,0.002137988,0.0008639499,0.000615207,0.0006399152,0.00007749812,0.0003467843],"genre_scores_gemma":[0.9906633,0.003238268,0.001080803,0.001046656,0.000505269,0.00008848908,0.0003115067,0.000009226075,0.003056455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2935767,"threshold_uncertainty_score":0.9999129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1514321860700101,"score_gpt":0.3473533253018432,"score_spread":0.195921139231833,"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."}}