{"id":"W3016247609","doi":"10.1186/s12917-020-02312-8","title":"Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits","year":2020,"lang":"en","type":"article","venue":"BMC Veterinary Research","topic":"Microbial infections and disease research","field":"Immunology and Microbiology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"Ministerio de Agricultura, Alimentación y Medio Ambiente; University of Minnesota","keywords":"Autoregressive integrated moving average; Animal husbandry; Stock (firearms); Livestock; Warning system; Dairy cattle; Time series; Seasonality; Geography; Statistics; Computer science; Mathematics","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":[0.0003745997,0.0001798123,0.0002568198,0.0001072736,0.0008134574,0.00005021161,0.0002367583,0.0001562076,0.0001507392],"category_scores_gemma":[0.0001388441,0.0001160165,0.000069399,0.0002377795,0.0008820787,0.00009071665,0.0002761469,0.0007467374,0.00001801068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004703109,"about_ca_system_score_gemma":0.0001144505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001075971,"about_ca_topic_score_gemma":0.0003783347,"domain_scores_codex":[0.9973225,0.001264404,0.0002741149,0.0004853793,0.0001704144,0.0004832014],"domain_scores_gemma":[0.9983961,0.001006001,0.00005655268,0.000267179,0.000119668,0.0001544998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005632486,0.0004409007,0.03199236,0.0001266905,0.0001309719,0.00002882227,0.001974345,0.000007974992,0.9574741,0.0005690784,0.0001012081,0.001521057],"study_design_scores_gemma":[0.00199136,0.01256305,0.9019895,0.0002526558,0.00004581387,0.0001285612,0.002961954,0.0000705834,0.07901992,0.0001113102,0.0005590209,0.0003063284],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978498,0.0003329364,0.0002942812,0.000543886,0.00004342493,0.0005017312,0.0002765082,0.00003177609,0.0001256835],"genre_scores_gemma":[0.9994565,0.00008613233,0.00008968022,0.00001083441,0.00009838237,0.00008467101,0.00009599895,0.00001927045,0.00005857479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8784542,"threshold_uncertainty_score":0.6256539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2703180209861681,"score_gpt":0.3951667423627825,"score_spread":0.1248487213766144,"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."}}