{"id":"W4399976386","doi":"10.1007/978-3-031-59131-0_10","title":"Time Series Forecasting for Personal Protective Equipment During COVID-19 Pandemic: A Case Study of Quebec","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Infection Control and Ventilation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Personal protective equipment; Coronavirus disease 2019 (COVID-19); Pandemic; Series (stratigraphy); 2019-20 coronavirus outbreak; Time series; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; Computer science; Medicine; Machine learning; Biology; Internal 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001815008,0.0002659781,0.0003828469,0.0003812044,0.0002417303,0.0000346174,0.0002770165,0.0002889434,0.000005228726],"category_scores_gemma":[0.0007713276,0.0002239134,0.00006221517,0.00007872092,0.0001266078,0.0000747106,0.0006089013,0.0006417546,0.000001488949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805794,"about_ca_system_score_gemma":0.00007633724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000245299,"about_ca_topic_score_gemma":0.00120898,"domain_scores_codex":[0.9990939,0.000007949962,0.0002934473,0.0003553722,0.0001152925,0.0001339661],"domain_scores_gemma":[0.9981036,0.0004169865,0.0001388566,0.001218985,0.00008862941,0.00003292135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005179672,0.003202104,0.002476038,0.0384144,0.01704499,0.002258201,0.04579438,0.02232883,0.01501182,0.0364671,0.0007027235,0.8111197],"study_design_scores_gemma":[0.009149348,0.008994453,0.0001523699,0.007593467,0.005878242,0.02155249,0.009640112,0.8417125,0.001333987,0.01116244,0.0798027,0.003027858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3819709,0.07050671,0.4107352,0.03695432,0.001140526,0.0540713,0.01188392,0.0279794,0.004757707],"genre_scores_gemma":[0.9949077,0.0001947642,0.001678347,0.00001491718,0.00004176985,0.0003426009,0.00041689,0.00005314443,0.002349874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8193837,"threshold_uncertainty_score":0.9130923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0802927943144194,"score_gpt":0.3116344043221534,"score_spread":0.2313416100077341,"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."}}