{"id":"W2398076896","doi":"10.1080/03155986.2004.11732690","title":"Screening Alternatives In Multiple Criteria Subset Selection","year":2004,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Wilfrid Laurier University","funders":"","keywords":"Knapsack problem; Selection (genetic algorithm); Mathematical optimization; Class (philosophy); Extension (predicate logic); Continuous knapsack problem; Context (archaeology); Computer science; Mathematics; Relation (database); Machine learning; Artificial intelligence; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0109089,0.0001481638,0.0002564068,0.001871694,0.0005930439,0.003371353,0.000452315,0.0001198803,0.0001706739],"category_scores_gemma":[0.007567307,0.0001164629,0.00004341487,0.001395814,0.0001299031,0.006502251,0.0002170986,0.0003422688,0.0004143985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806558,"about_ca_system_score_gemma":0.0002483665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001203459,"about_ca_topic_score_gemma":0.0002554977,"domain_scores_codex":[0.9941851,0.0003400413,0.001470213,0.0002773789,0.003338375,0.0003889017],"domain_scores_gemma":[0.9958879,0.001526222,0.0001951202,0.0002580407,0.001977976,0.0001547631],"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":[0.001128681,0.0001977632,0.1865642,0.0001636886,0.00006926991,0.00002552339,0.03517767,0.3351054,0.005608021,0.2649386,0.01367343,0.1573478],"study_design_scores_gemma":[0.004345236,0.0001928595,0.1813455,0.0003057232,0.000001623762,0.0001180128,0.01199939,0.6269307,0.0008187052,0.004243707,0.1692591,0.0004395162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8915163,0.0001061784,0.1001664,0.000874378,0.0005396971,0.00116189,0.0001027294,0.00004672922,0.005485747],"genre_scores_gemma":[0.9950916,0.00001394225,0.004087585,0.0001864644,0.0001363527,0.0001165886,0.00004874581,0.000007049393,0.0003116269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2918254,"threshold_uncertainty_score":0.9976633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3830377451476857,"score_gpt":0.5150064264165563,"score_spread":0.1319686812688705,"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."}}