{"id":"W2294929898","doi":"10.5555/2872550.2872557","title":"Automatic validation for multi criteria decision making models in simulation environments","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Research in Systems and Signal Processing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Decision support system; Evidential reasoning approach; Computer science; Decision analysis; Decision model; Decision engineering; Business decision mapping; R-CAST; Multiple-criteria decision analysis; Decision field theory; Decision-making models; Optimal decision; Measure (data warehouse); Decision tree; Data mining; Machine learning; Artificial intelligence; Operations research; Engineering; Mathematics; 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":[0.0184009,0.001173117,0.001031088,0.001797627,0.001051576,0.001785203,0.001313639,0.001306186,0.002148111],"category_scores_gemma":[0.06231674,0.0008782152,0.001338952,0.0009690339,0.001258993,0.002286748,0.002102434,0.002005416,0.0004303087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001081001,"about_ca_system_score_gemma":0.00153215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002582787,"about_ca_topic_score_gemma":0.001655552,"domain_scores_codex":[0.9813477,0.01337684,0.0009858576,0.0008655114,0.003086837,0.0003373992],"domain_scores_gemma":[0.9294189,0.05661014,0.003436873,0.006064605,0.004142601,0.000326793],"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":[0.001069042,0.0003567385,0.00741586,0.000324204,0.0002241109,0.0002034285,0.000911572,0.8301039,0.01142559,0.0294488,0.0006975514,0.1178192],"study_design_scores_gemma":[0.00002716651,0.00005244269,0.0002520726,0.00002373183,0.000008040828,0.00001659407,0.00002990432,0.9910431,0.00336856,0.004804431,0.0003616131,0.00001234023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0434832,0.00004970103,0.9539773,0.00008269672,0.00002528325,0.0001341237,0.00005920216,0.001462703,0.0007256012],"genre_scores_gemma":[0.6006375,0.00005363364,0.3980746,0.00004175224,0.00001051235,0.0004089537,0.0002795547,0.0001741943,0.0003193554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0184009,"threshold_uncertainty_score":0.09731448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530963023879873,"score_gpt":0.4008978534105388,"score_spread":0.2478015510225515,"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."}}