{"id":"W2791996323","doi":"10.1016/j.prevetmed.2018.01.006","title":"Cow- and herd-level factors associated with lameness in small-scale grazing dairy herds in Brazil","year":2018,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of British Columbia","funders":"Universidade Federal de Santa Catarina; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Dairy Management","keywords":"Lameness; Herd; Odds ratio; Confidence interval; Animal science; Logistic regression; Incidence (geometry); Veterinary medicine; Medicine; Dairy cattle; Mastitis; Grazing; Ice calving; Demography; Biology; Mathematics; Lactation; Pregnancy; Internal medicine; Surgery; Ecology","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":[0.000457698,0.0001702249,0.0002554208,0.0005585327,0.0004930821,0.0004139245,0.0003987358,0.0002984019,0.0008850465],"category_scores_gemma":[0.001892237,0.0002434484,0.0003409014,0.0007128124,0.0004893276,0.0002551706,0.000369593,0.0002934078,0.00005878737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008701796,"about_ca_system_score_gemma":0.000788978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.093265,"about_ca_topic_score_gemma":0.189022,"domain_scores_codex":[0.9996961,0.00009168347,0.00002507627,0.00005661062,0.00004334493,0.00008717998],"domain_scores_gemma":[0.9990898,0.0002085855,0.0003855768,0.00004753831,0.00009572828,0.00017279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002032649,0.00003530966,0.998269,0.000009354754,0.00002503811,0.00004529437,0.0005396127,0.00001326948,0.0002110954,0.00002717433,0.0000296521,0.0007749276],"study_design_scores_gemma":[0.000001077785,0.0000332169,0.9987302,0.000009766636,0.00001172834,0.00004392336,0.001014752,0.00006122355,0.00001377211,0.00001188929,0.00006699148,0.000001400734],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996533,0.0001040169,0.00001579256,0.00002829856,0.00000121426,0.000002710816,0.00003620725,6.293775e-7,0.0001579104],"genre_scores_gemma":[0.9997934,0.00007332096,0.00003047464,0.000008530891,0.000001252351,0.000002385088,0.00003224158,4.526272e-7,0.00005787628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.093265,"threshold_uncertainty_score":0.1854443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1699395144275082,"score_gpt":0.3753367347643611,"score_spread":0.205397220336853,"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."}}