{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007143259,0.001642588,0.001758604,0.004222836,0.0008492902,0.001538858,0.001070764,0.001185331,0.003231417],"category_scores_gemma":[0.02099963,0.0006631355,0.001257409,0.004792551,0.001834744,0.001940339,0.001601509,0.001382498,0.0004963254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008591209,"about_ca_system_score_gemma":0.001383849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001082477,"about_ca_topic_score_gemma":0.001468168,"domain_scores_codex":[0.9879288,0.008827122,0.0002934145,0.0004482233,0.002292412,0.0002101562],"domain_scores_gemma":[0.98474,0.01302858,0.0006310851,0.0004209162,0.001021271,0.0001581464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002795039,0.0002153008,0.001457718,0.001175006,0.0002032446,0.0004830823,0.000705748,0.3923368,0.004987278,0.1399621,0.002976134,0.4552181],"study_design_scores_gemma":[0.00009094704,0.0003935263,0.0005752181,0.0003183146,0.00008935057,0.0002385811,0.0001635257,0.833187,0.004634927,0.1520183,0.008228583,0.00006168063],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007967034,0.001043393,0.987599,0.0001755606,0.00003404936,0.0001881486,0.00003733753,0.00008432764,0.002871084],"genre_scores_gemma":[0.1698057,0.001760065,0.8244661,0.0001357547,0.00009954579,0.0009507721,0.0001507978,0.00005167807,0.002579618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007143259,"threshold_uncertainty_score":0.0377776,"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."}}