{"id":"W4388440709","doi":"10.1016/j.cie.2023.109715","title":"Capacity acquisition and PPE distribution planning during the COVID-19 pandemic","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Pooling; Pandemic; Personal protective equipment; Coronavirus disease 2019 (COVID-19); Economic shortage; Capacity planning; Supply and demand; Business; Distribution (mathematics); Operations research; Computer science; Operations management; Risk analysis (engineering); Engineering; Economics; Microeconomics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001298461,0.0003741707,0.0002709523,0.001055977,0.001032922,0.002052628,0.0006394418,0.001282647,0.007851661],"category_scores_gemma":[0.006297966,0.000371779,0.0002344978,0.0009146743,0.0006060289,0.002043187,0.001204925,0.001857729,0.0003719706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005634712,"about_ca_system_score_gemma":0.005322167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1687338,"about_ca_topic_score_gemma":0.2051044,"domain_scores_codex":[0.9992977,0.0001877425,0.00002355734,0.00007085445,0.0001295147,0.0002906008],"domain_scores_gemma":[0.9984965,0.0004859366,0.0001477583,0.00004277581,0.0004563123,0.0003706218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007637304,0.0002812213,0.06515388,0.0001430045,0.00008083283,0.001790372,0.001718621,0.7844331,0.001587056,0.04496608,0.03105917,0.06802288],"study_design_scores_gemma":[0.0001038776,0.0004524633,0.1045657,0.0002679463,0.00004872039,0.0003292692,0.02166977,0.7897083,0.002068093,0.04243905,0.03819579,0.0001511186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8893255,0.0004502059,0.009483836,0.01374572,0.0001986645,0.0003106307,0.003888442,0.0002322373,0.08236478],"genre_scores_gemma":[0.9936976,0.0001163474,0.001606971,0.0001327474,0.0000170684,0.00003425831,0.0004103983,0.00001977117,0.003964871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1687338,"threshold_uncertainty_score":0.3355034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05588505654443147,"score_gpt":0.2441048067188686,"score_spread":0.1882197501744372,"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."}}