{"id":"W4394692120","doi":"10.3389/fvets.2024.1392166","title":"The Canadian Cow-Calf Surveillance Network – productivity and health summary 2018 to 2022","year":2024,"lang":"en","type":"article","venue":"Frontiers in Veterinary Science","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Calgary; University of Saskatchewan","funders":"Beef Cattle Research Council; Zoetis; Ministry of Agriculture - Saskatchewan","keywords":"Productivity; Cow-calf; Business; Animal science; Biology; Economics; Economic growth","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00229063,0.001189334,0.0007409719,0.004797835,0.001439705,0.00153102,0.002181963,0.0006556848,0.01006045],"category_scores_gemma":[0.005952096,0.00038248,0.000828681,0.005799103,0.000259114,0.0005458419,0.001143415,0.001068378,0.002952777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03527816,"about_ca_system_score_gemma":0.08039626,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.990535,"about_ca_topic_score_gemma":0.9918555,"domain_scores_codex":[0.997543,0.000121026,0.0001683894,0.0001972975,0.001507989,0.0004623123],"domain_scores_gemma":[0.9884946,0.0001324916,0.000472209,0.0001158646,0.00949266,0.001292217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001728042,0.00004452922,0.05917414,0.0007610135,0.0001291608,0.00007556862,0.00009750995,0.0005088287,0.000256109,0.0005810584,0.8841047,0.05409461],"study_design_scores_gemma":[0.0001403011,0.0000744463,0.5747775,0.001204365,0.0001492237,0.0001177997,0.0003453244,0.002565288,0.0003059111,0.0003013785,0.4199459,0.00007249284],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01550599,0.00594225,0.001155078,0.006028153,0.001001716,0.0007199513,0.943395,0.0005509839,0.02570083],"genre_scores_gemma":[0.07677239,0.007658958,0.004024666,0.003651502,0.0004460373,0.00108568,0.8874577,0.0001217728,0.01878125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03527816,"threshold_uncertainty_score":0.2559622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03860164445090455,"score_gpt":0.2757064338570994,"score_spread":0.2371047894061949,"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."}}