{"id":"W4220672502","doi":"10.1007/s10479-022-04543-4","title":"Optimal multi-stage group partition for efficient coronavirus screening","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; University of Manitoba","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Wilfrid Laurier University","keywords":"Partition (number theory); Stage (stratigraphy); Economic shortage; Computer science; Coronavirus disease 2019 (COVID-19); Group testing; Scheme (mathematics); Group (periodic table); Coronavirus; Multi stage; Tree (set theory); Mathematical optimization; Operations research; Mathematics; Medicine; Engineering; Combinatorics; Industrial engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005939397,0.0000978024,0.0002502941,0.0001524076,0.001455486,0.00003490016,0.0002770055,0.0000364614,0.0004812054],"category_scores_gemma":[0.007251255,0.00008380641,0.0001255412,0.0003779161,0.0001705356,0.00006117647,0.0005164058,0.0003022342,0.000008812935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005609864,"about_ca_system_score_gemma":0.00005502379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003452398,"about_ca_topic_score_gemma":0.0001413238,"domain_scores_codex":[0.9976389,0.000641632,0.0004168975,0.0002984032,0.0005702933,0.0004338732],"domain_scores_gemma":[0.9959724,0.003089139,0.00004391449,0.0002819067,0.0005407691,0.00007188445],"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":[0.0003230523,0.001747185,0.0009614321,0.0001784765,0.000131064,0.000008242053,0.001484697,0.7828289,0.005045745,0.1856431,0.0179008,0.003747332],"study_design_scores_gemma":[0.00128777,0.001707678,0.005210121,0.00004194278,0.00001794265,0.000002262708,0.00386345,0.9262044,0.003568088,0.003373737,0.05439928,0.0003233144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.766266,0.0004282632,0.2239261,0.006800563,0.00005673206,0.001757747,0.0004789353,0.00006303166,0.0002225395],"genre_scores_gemma":[0.9419324,0.00004938112,0.05492074,0.0002740808,0.00004710098,0.001486742,0.00005731084,0.00001746431,0.001214815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1822693,"threshold_uncertainty_score":0.9998445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8785268876190176,"score_gpt":0.6386875129358051,"score_spread":0.2398393746832126,"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."}}