{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002302957,0.0002029108,0.0003808009,0.0001127694,0.0001861365,0.0001469179,0.0005904484,0.00009781359,0.00001622415],"category_scores_gemma":[0.002531122,0.000221475,0.00005226497,0.0004955372,0.00003352631,0.000459137,0.0002928392,0.001369997,0.0001184627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001229868,"about_ca_system_score_gemma":0.0008288949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001035907,"about_ca_topic_score_gemma":0.0002095727,"domain_scores_codex":[0.9969834,0.00002146087,0.000598714,0.00062904,0.00007690422,0.001690487],"domain_scores_gemma":[0.9987842,0.00006504943,0.0003628665,0.00046522,0.00004933014,0.0002733748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001294257,0.0002713436,0.3610794,0.0005865174,0.002842713,0.00005900961,0.003670424,0.001772928,0.02786787,0.2563644,0.009752742,0.3344384],"study_design_scores_gemma":[0.008070992,0.00429571,0.04465275,0.0001437296,0.0004016903,0.001221475,0.001894651,0.07076621,0.001422074,0.5586697,0.3048795,0.003581561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7922337,0.01193011,0.1801179,0.01385939,0.0004087828,0.0005024082,0.000731274,0.000103246,0.0001131494],"genre_scores_gemma":[0.9937272,0.004328526,0.0001385475,0.001022589,0.0005566755,0.000002841133,0.00006233893,0.00003193946,0.0001293179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3308568,"threshold_uncertainty_score":0.9031488,"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."}}