{"id":"W4206086126","doi":"10.1016/j.susoc.2022.01.003","title":"An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"Sustainable Operations and Computers","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Thriving; Data envelopment analysis; Investment (military); Operations research; 2019-20 coronavirus outbreak; Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Pharmaceutical industry; Business; Industrial organization; Econometrics; Economics; Engineering; Infectious disease (medical specialty); Mathematics; Statistics; Political science; Virology; Disease","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":[0.00226119,0.0006496264,0.0008968159,0.001158846,0.0005203866,0.002115275,0.000988538,0.001167936,0.001881328],"category_scores_gemma":[0.003914429,0.0003456301,0.000888341,0.001292762,0.0006436037,0.001396719,0.0007901596,0.001029879,0.0001407402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002551938,"about_ca_system_score_gemma":0.001839295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01841756,"about_ca_topic_score_gemma":0.008765596,"domain_scores_codex":[0.9990615,0.0004689509,0.00004905507,0.0001601763,0.0001284275,0.0001319244],"domain_scores_gemma":[0.9983357,0.001162465,0.0001755286,0.00003911597,0.000222802,0.00006443931],"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":[0.00003421916,0.00003104117,0.0009043289,0.00001765634,0.00001815774,0.00004331425,0.00003951871,0.9874138,0.0001215258,0.007910154,0.0001555077,0.003310762],"study_design_scores_gemma":[0.00000225633,0.00001009465,0.0001532245,0.000002140598,0.000004547067,0.000003114998,0.00001248715,0.9986078,0.00003389378,0.001107169,0.00006083676,0.000002417044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3383295,0.0005685565,0.6460344,0.001081017,0.00005387296,0.0001916176,0.0003462237,0.0001786775,0.01321615],"genre_scores_gemma":[0.9797963,0.0002225195,0.01730709,0.00003333302,0.00001237167,0.0001215289,0.0001129853,0.00001277081,0.002381163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01841756,"threshold_uncertainty_score":0.03662074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1294319949007328,"score_gpt":0.4435789202628653,"score_spread":0.3141469253621325,"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."}}