{"id":"W1987852976","doi":"10.1155/2012/824265","title":"Evaluating Projects Based on Intuitionistic Fuzzy Group Decision Making","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Mathematics","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Group decision-making; Selection (genetic algorithm); TOPSIS; Aggregate (composite); Operator (biology); Computer science; Operations research; Group (periodic table); Fuzzy logic; Score; Mathematics; Management science; Mathematical optimization; Artificial intelligence; Machine learning; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0187037,0.0003058601,0.0007732263,0.001038116,0.0002967828,0.000608668,0.001094646,0.0001508741,0.0005722881],"category_scores_gemma":[0.01425158,0.0002064706,0.000281577,0.0009321797,0.00007571275,0.0004501344,0.0001988335,0.0004854083,0.0004408541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057683,"about_ca_system_score_gemma":0.000134423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.657798e-7,"about_ca_topic_score_gemma":5.442371e-7,"domain_scores_codex":[0.9912853,0.000143773,0.002593283,0.0003129128,0.005157753,0.0005070023],"domain_scores_gemma":[0.9839439,0.011631,0.002628788,0.0008506693,0.0007024613,0.0002432341],"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":[0.00148252,0.00275949,0.0004411782,0.0001950838,0.00008225622,0.00006354061,0.007639234,0.02188873,0.01811593,0.08950709,0.009253437,0.8485715],"study_design_scores_gemma":[0.002932185,0.0006919252,0.001615826,0.001851716,0.0001632342,0.0002644352,0.00397583,0.3044802,0.001064294,0.6803955,0.001896502,0.0006684365],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3116747,0.00005569043,0.6680898,0.00008041568,0.001366507,0.000515434,0.000005558567,0.00003607838,0.01817583],"genre_scores_gemma":[0.5597827,0.000001157852,0.4395983,0.0002360093,0.0003361623,0.000007035584,3.694685e-7,0.00002658246,0.0000116845],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8479031,"threshold_uncertainty_score":0.9940518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2856818903715865,"score_gpt":0.4824104865515948,"score_spread":0.1967285961800083,"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."}}