{"id":"W4403215990","doi":"10.1145/3699824.3699829","title":"Welfare-Maximizing Pooled Testing","year":2024,"lang":"en","type":"article","venue":"ACM SIGecom Exchanges","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Welfare; Econometrics; Computer science; Economics","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":[0.0004145113,0.0001753024,0.0001595154,0.000182277,0.0002424148,0.000557341,0.001180248,0.00005785767,0.00008024972],"category_scores_gemma":[0.0005653803,0.000151926,0.00006458173,0.0006505877,0.00002182073,0.0003618851,0.000694313,0.0003053514,0.0002708723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000028852,"about_ca_system_score_gemma":0.00003177048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000117194,"about_ca_topic_score_gemma":0.000008477138,"domain_scores_codex":[0.9986587,0.00007311819,0.0001663017,0.0004916143,0.0002338383,0.0003764354],"domain_scores_gemma":[0.9986061,0.0004920526,0.00004075259,0.0007204844,0.00004476916,0.00009580836],"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":[7.519964e-7,0.00001854024,0.0004665158,0.00008456479,0.00003194752,0.0001772896,0.001220045,0.00011483,0.0007082876,0.01198905,0.002028971,0.9831592],"study_design_scores_gemma":[0.0003178137,0.0002756568,0.0064302,0.0004154933,0.00003841104,0.0002418007,0.0001854081,0.6552356,0.001594341,0.01722658,0.3171295,0.0009091879],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05902001,0.02424415,0.6384712,0.1969149,0.01185289,0.0007131152,0.00001536283,0.01357149,0.05519694],"genre_scores_gemma":[0.8575629,0.00002974948,0.1395177,0.0004862672,0.0006636808,0.00002577891,0.000004045345,0.00003202853,0.001677897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.98225,"threshold_uncertainty_score":0.6195363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03299751274314557,"score_gpt":0.2779883496096617,"score_spread":0.2449908368665162,"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."}}