{"id":"W4220845611","doi":"10.1108/bfj-03-2021-0333","title":"Food security and disruptions of the global food supply chains during COVID-19: building smarter food supply chains for post COVID-19 era","year":2022,"lang":"en","type":"article","venue":"British Food Journal","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Food security; Supply chain; Coronavirus disease 2019 (COVID-19); Pandemic; Food supply; Resilience (materials science); Business; Food insecurity; Food chain; Food systems; Agriculture; Economic growth; Economics; Marketing; Agricultural economics; Geography; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001590135,0.0005167287,0.0002802486,0.002009079,0.002527979,0.005296177,0.0007457656,0.001118435,0.002814849],"category_scores_gemma":[0.002801237,0.0002140506,0.0005326401,0.00357818,0.002309576,0.004368917,0.002670203,0.001305177,0.0002146389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01105381,"about_ca_system_score_gemma":0.01706666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1158059,"about_ca_topic_score_gemma":0.2019443,"domain_scores_codex":[0.9988906,0.0003907771,0.00004992078,0.0001177406,0.0002031477,0.0003479305],"domain_scores_gemma":[0.9980331,0.0006795519,0.0004751048,0.00007433874,0.0005438876,0.0001941485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002675679,0.0004531323,0.2733459,0.0112578,0.0005086499,0.01266624,0.06797368,0.02324249,0.006173843,0.1964976,0.02994994,0.377663],"study_design_scores_gemma":[0.00002761238,0.0004855251,0.2308073,0.01215699,0.0004259173,0.002076929,0.4056982,0.00897505,0.005587907,0.05986929,0.2736959,0.0001933701],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7603498,0.04291768,0.01561888,0.04423308,0.0007013456,0.0007765369,0.001843264,0.0001258336,0.1334336],"genre_scores_gemma":[0.9596097,0.02770506,0.006971647,0.001585979,0.00006081713,0.0001409563,0.0003492637,0.000013721,0.003562821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1158059,"threshold_uncertainty_score":0.2302637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02925905739368486,"score_gpt":0.2639721875985656,"score_spread":0.2347131302048807,"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."}}