{"id":"W3129465294","doi":"10.1016/j.meatsci.2021.108459","title":"The Covid-19 pandemic and meat supply chains","year":2021,"lang":"en","type":"article","venue":"Meat Science","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":138,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Supply chain; Pandemic; Food supply; Business; Adaptability; Scope (computer science); Vulnerability (computing); Coronavirus disease 2019 (COVID-19); Resilience (materials science); Food processing; Food industry; Food systems; Industrial organization; Meat packing industry; Marketing; Economics; Food security; Agriculture; Food science; Agricultural economics; Geography; Computer science","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":[0.002356144,0.0003611813,0.0004730785,0.001049623,0.000739047,0.003171749,0.0003773932,0.003519239,0.01535461],"category_scores_gemma":[0.01563794,0.0003518253,0.0004661035,0.002077376,0.001907838,0.004466158,0.001279677,0.003092354,0.0005482072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003241193,"about_ca_system_score_gemma":0.00175213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0248587,"about_ca_topic_score_gemma":0.01873837,"domain_scores_codex":[0.9993256,0.0003263698,0.0000204313,0.00007371965,0.0001026144,0.0001512626],"domain_scores_gemma":[0.9919806,0.004996474,0.001419392,0.0002582934,0.000806384,0.0005389475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0004479875,0.0002410604,0.04675889,0.0002644556,0.0001439949,0.001005415,0.0005456427,0.2022661,0.0005075014,0.6702991,0.03860151,0.03891831],"study_design_scores_gemma":[0.00006012077,0.0001037504,0.0167193,0.0002591436,0.00004144246,0.0001446185,0.001783434,0.132501,0.000236316,0.8191497,0.02892914,0.00007207724],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5891091,0.0200309,0.05140699,0.1742471,0.001570043,0.0001239212,0.003827071,0.0001300672,0.1595547],"genre_scores_gemma":[0.9789296,0.005622521,0.0009675014,0.001212099,0.0005028149,0.00002419829,0.0003374925,0.00001802522,0.01238574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0248587,"threshold_uncertainty_score":0.05136627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09028513716285759,"score_gpt":0.299820303674678,"score_spread":0.2095351665118205,"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."}}