{"id":"W4256404100","doi":"10.32920/ryerson.14654448","title":"Food supply chain resilience in an era of climate change: recommendations for Toronto, Ontario","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Food Waste Reduction and Sustainability","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Resilience (materials science); Climate change; Supply chain; Food security; Adaptation (eye); Business; Food supply; Psychological resilience; Natural resource economics; Climate change adaptation; Environmental resource management; Threatened species; Food chain; Food systems; Environmental planning; Environmental economics; Geography; Economics; Marketing; Agricultural economics","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":[0.002660693,0.001483993,0.0007939602,0.002087447,0.005324041,0.005036842,0.002463708,0.00219563,0.0233453],"category_scores_gemma":[0.00510281,0.0006050253,0.001376881,0.005546886,0.00200156,0.003490838,0.003079489,0.001458562,0.003383386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07528047,"about_ca_system_score_gemma":0.2297771,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9816962,"about_ca_topic_score_gemma":0.9929919,"domain_scores_codex":[0.9983301,0.000226063,0.0001145842,0.00009873268,0.0006491138,0.0005814835],"domain_scores_gemma":[0.9899644,0.0004748893,0.0004169816,0.0003028525,0.005911882,0.002929093],"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":[0.00009099472,0.00006882622,0.01204197,0.002211572,0.00006598167,0.0008023171,0.003256472,0.006309898,0.001488307,0.01664286,0.8430225,0.1139983],"study_design_scores_gemma":[0.00005520737,0.00004009785,0.0319503,0.002295182,0.0001186744,0.0001720274,0.01474554,0.002073456,0.0006343906,0.006423922,0.941367,0.0001240965],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.03692446,0.1057223,0.0175627,0.585228,0.008419574,0.001750501,0.02573927,0.002759679,0.2158936],"genre_scores_gemma":[0.2991847,0.1999669,0.1072071,0.02503636,0.001415498,0.002221475,0.02099321,0.0008732574,0.3431016],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07528047,"threshold_uncertainty_score":0.5462006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05151032232011835,"score_gpt":0.2921339904725706,"score_spread":0.2406236681524523,"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."}}