{"id":"W6958615454","doi":"10.6084/m9.figshare.12127650","title":"Additional file 3 of 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":"Figshare","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Stock (firearms); Autoregressive integrated moving average; Dairy cattle; Dairy farming; Stock assessment","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00158832,0.0007249428,0.0007505367,0.001761113,0.0006309451,0.001119954,0.001474752,0.0007142363,0.8075715],"category_scores_gemma":[0.01547822,0.0004349186,0.0006295962,0.003308153,0.0001708738,0.001289053,0.0009164804,0.0005207967,0.1503373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008271919,"about_ca_system_score_gemma":0.001114226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02558363,"about_ca_topic_score_gemma":0.04140383,"domain_scores_codex":[0.9993886,0.00013068,0.00008948093,0.0001482283,0.000139245,0.0001037987],"domain_scores_gemma":[0.9881864,0.00803173,0.000799679,0.0007121634,0.001938318,0.0003317157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001262641,0.00004135846,0.004633494,0.0006745463,0.00002295136,0.00003689621,0.00007101141,0.000463392,0.00006430181,0.0003959059,0.9861085,0.007361434],"study_design_scores_gemma":[0.002980275,0.0001643758,0.08369932,0.00197415,0.0001394926,0.0002899263,0.0009686573,0.003474015,0.0009099715,0.00641034,0.8988561,0.0001332806],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0001974088,0.000004921114,0.0002131183,0.00004937641,0.00001016008,0.00003674508,0.9982617,0.0002437246,0.0009828586],"genre_scores_gemma":[0.01048132,0.00006074257,0.005135811,0.0003311762,0.00006204715,0.001020774,0.9717826,0.0009458318,0.01017979],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1924285,"threshold_uncertainty_score":0.2744758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06087696032174591,"score_gpt":0.2643471602271052,"score_spread":0.2034701999053593,"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."}}