{"id":"W2091305937","doi":"10.1080/0740817x.2010.504684","title":"An efficient dynamic optimization method for sequential identification of group-testable items","year":2010,"lang":"en","type":"article","venue":"IIE Transactions","topic":"SARS-CoV-2 detection and testing","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Saint Mary's University","funders":"","keywords":"Dynamic programming; Mathematical optimization; Group (periodic table); Computation; Identification (biology); Stochastic programming; Computer science; Group testing; Scheme (mathematics); Linear programming; Algorithm; Mathematics","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":[0.000391529,0.00009406476,0.0001511284,0.0001900724,0.0001709299,0.00002510431,0.00005422668,0.0001009162,0.00007801681],"category_scores_gemma":[0.00006892671,0.0000972799,0.0001033329,0.0002993518,0.00004172143,0.0001164279,0.000001089772,0.0001721577,0.000003897434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003794681,"about_ca_system_score_gemma":0.00005036422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001076675,"about_ca_topic_score_gemma":0.0001313844,"domain_scores_codex":[0.9991241,0.0000330135,0.0003395519,0.0002237564,0.000136726,0.0001428708],"domain_scores_gemma":[0.9992453,0.0001105902,0.0001242353,0.000252311,0.0002165473,0.00005103943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006712569,0.0002765492,0.00001746165,0.00004769845,0.00002427788,3.891292e-7,0.000153296,0.06578889,0.9268292,0.00007733449,0.000001795285,0.006716035],"study_design_scores_gemma":[0.0005915373,0.0000926342,0.0001932516,0.000009931369,0.0001268126,0.00003667741,0.00007231998,0.6350892,0.3635571,0.00003013159,0.0001406438,0.0000597588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2466992,0.00000779822,0.7520355,0.00006285243,0.0005449312,0.0004081288,0.00002879227,0.0001223115,0.00009048342],"genre_scores_gemma":[0.8574191,0.000001251722,0.1422719,0.00004406488,0.00004964755,0.00007456956,0.00004483254,0.00002351153,0.00007110574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6107199,"threshold_uncertainty_score":0.3966959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372448117591032,"score_gpt":0.3486940523802195,"score_spread":0.3249695712043092,"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."}}