{"id":"W1491353118","doi":"10.1007/978-3-642-31900-6_49","title":"Multiple Criteria Decision Analysis with Game-Theoretic Rough Sets","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Rough set; Computer science; Context (archaeology); Extension (predicate logic); Decision rule; Game theory; Feature (linguistics); Dominance-based rough set approach; Artificial intelligence; Operations research; Data mining; Mathematical economics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001054645,0.0007427247,0.0009137749,0.001395159,0.0002767435,0.0009392785,0.003985938,0.0003793628,0.00009920722],"category_scores_gemma":[0.00008014754,0.0005384587,0.0002690826,0.001971078,0.000877931,0.0009178627,0.001489985,0.0007283703,0.00008782558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002471371,"about_ca_system_score_gemma":0.000259635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003532489,"about_ca_topic_score_gemma":0.0001349912,"domain_scores_codex":[0.9949366,0.00006101718,0.0006078159,0.001948851,0.001408996,0.001036665],"domain_scores_gemma":[0.995746,0.000870852,0.00035992,0.002415941,0.0002732406,0.0003341135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002638066,0.00005819272,0.0007618809,0.00002421761,0.00008284995,0.0001361241,0.001059364,0.03884444,0.00001108496,0.007617717,0.00002174172,0.951356],"study_design_scores_gemma":[0.000404755,0.000257268,0.001632118,0.0002146086,0.0001158317,0.00009464335,1.732675e-7,0.8870001,0.00009884345,0.1079901,0.00123514,0.0009563949],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005140599,0.0008786615,0.9942402,0.0002637079,0.001030769,0.0003651655,0.00001033391,0.0001821959,0.002514928],"genre_scores_gemma":[0.4599654,0.00006081973,0.5389764,0.000711804,0.0002099596,0.000007364326,0.000009382369,0.00002763651,0.00003126072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9503996,"threshold_uncertainty_score":0.9997067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646101339085808,"score_gpt":0.2559294080955705,"score_spread":0.2394683947047124,"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."}}