{"id":"W2082627727","doi":"10.1109/icsmc.2006.384523","title":"A Flexible Multiple Criteria Sorting Method with Application in Inventory Management","year":2006,"lang":"en","type":"article","venue":"","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Sorting; Flexibility (engineering); Computer science; Decision maker; Inventory management; Sorting algorithm; Card sorting; Operations research; Artificial intelligence; Machine learning; Data mining; Operations management; Algorithm; Engineering; Mathematics; Systems engineering; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007099133,0.001897242,0.002120413,0.005376103,0.00234826,0.001963458,0.001914217,0.001979274,0.006181311],"category_scores_gemma":[0.01223427,0.0008996323,0.001990084,0.009167725,0.001930164,0.002389454,0.001892036,0.002006582,0.001302084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066493,"about_ca_system_score_gemma":0.002113672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003010708,"about_ca_topic_score_gemma":0.003029498,"domain_scores_codex":[0.9921845,0.003373771,0.0004603679,0.0005774588,0.003165757,0.0002383374],"domain_scores_gemma":[0.9952943,0.002995264,0.0003318201,0.0003182309,0.0009093938,0.000150923],"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":[0.0002858571,0.0002123635,0.0007024446,0.0007354082,0.0001754814,0.0004930794,0.0006597402,0.1754395,0.009402585,0.07622092,0.00398971,0.7316829],"study_design_scores_gemma":[0.0001658894,0.0003812942,0.0007474696,0.0002400205,0.00008627703,0.0005220252,0.0001808628,0.8457265,0.006540413,0.1169186,0.02823419,0.0002564863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001850964,0.0002154211,0.9959266,0.00008315763,0.00005760633,0.0001660314,0.00003124982,0.0002334922,0.001435574],"genre_scores_gemma":[0.02553396,0.000220653,0.9726737,0.00007012856,0.00003792132,0.000283911,0.00005005653,0.00006621049,0.001063475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007099133,"threshold_uncertainty_score":0.03754425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1200973914943832,"score_gpt":0.4463184851782772,"score_spread":0.3262210936838941,"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."}}