{"id":"W3157915244","doi":"10.1111/cjag.12287","title":"Risk management in Canada's agricultural sector in light of COVID‐19: Considerations one year later","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Ministry of Agriculture, Food and Rural Affairs","funders":"","keywords":"Agriculture; Government (linguistics); Pandemic; Coronavirus disease 2019 (COVID-19); Business; Risk management; Public sector; Economic growth; Economic policy; Economics; Finance; Geography; Economy; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005835766,0.0003551994,0.001171274,0.0007719615,0.0001279275,0.0001212795,0.0004298824,0.0001724686,0.001567183],"category_scores_gemma":[0.0006237768,0.0003747522,0.0002495638,0.0004082945,0.00006854727,0.0005286948,0.00005037398,0.0005227649,0.00004021571],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01338074,"about_ca_system_score_gemma":0.004737847,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9220166,"about_ca_topic_score_gemma":0.999703,"domain_scores_codex":[0.9961731,0.00007386045,0.002224891,0.0005515249,0.00001842423,0.000958127],"domain_scores_gemma":[0.9961638,0.000227782,0.001410303,0.0003608799,0.0001465272,0.001690727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007907507,0.0001305871,0.6618734,0.0003478403,0.001122102,0.001304646,0.006810707,0.07971495,0.00009955189,0.2320519,0.01601426,0.00045093],"study_design_scores_gemma":[0.00280646,0.0001033248,0.9388877,0.0001279179,0.00005451601,0.0003840283,0.005109218,0.000110825,0.0001755272,0.01908348,0.03231427,0.0008427335],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806374,0.001135859,0.000004573718,0.009091923,0.001109443,0.000325461,0.0009659297,0.000004343262,0.006725025],"genre_scores_gemma":[0.9972079,0.0003999455,0.0003887334,0.001020754,0.0002178905,0.00001385933,0.00005825407,0.00002591429,0.0006667158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2770143,"threshold_uncertainty_score":0.9998704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03422983543778222,"score_gpt":0.1799488181621589,"score_spread":0.1457189827243767,"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."}}