{"id":"W3116478932","doi":"10.2139/ssrn.3742121","title":"Data Analytics to Detect Panic Buying and Improve Products Distribution Amid Pandemic","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Bell (Canada); Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Pandemic; Analytics; Panic; Coronavirus disease 2019 (COVID-19); Distribution (mathematics); Business; Data science; Computer science; Psychology; Mathematics; Medicine","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":[0.002220151,0.0008278815,0.0005556602,0.004718781,0.0003533578,0.001915213,0.0008586805,0.0009940829,0.0041356],"category_scores_gemma":[0.01639934,0.0002915929,0.0004726122,0.003431027,0.0003075257,0.002649547,0.0009302217,0.001336749,0.00164103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007080525,"about_ca_system_score_gemma":0.0008736848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00494811,"about_ca_topic_score_gemma":0.004727795,"domain_scores_codex":[0.9989617,0.0003300875,0.000117075,0.0001931817,0.0002729578,0.0001250873],"domain_scores_gemma":[0.9867035,0.007404832,0.002012263,0.001205217,0.002030284,0.000643814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001828096,0.000966081,0.6611915,0.0005376717,0.0004558932,0.0005747454,0.0003732446,0.05354916,0.004162791,0.009337437,0.07605406,0.1909694],"study_design_scores_gemma":[0.000187496,0.0004328699,0.2001343,0.0002421458,0.0001568588,0.0003352301,0.002146485,0.7233673,0.005008033,0.04390095,0.02399575,0.00009253277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8041248,0.003121226,0.06396854,0.01689831,0.001161495,0.0008229176,0.07101397,0.007623951,0.03126478],"genre_scores_gemma":[0.9606022,0.0004050373,0.01833765,0.0006420178,0.0002979923,0.0001120222,0.01776416,0.0001217422,0.001717193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00494811,"threshold_uncertainty_score":0.01383501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06712459214074912,"score_gpt":0.2692515057385242,"score_spread":0.2021269135977751,"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."}}