{"id":"W1986307640","doi":"10.1016/j.knosys.2008.03.028","title":"A new application of ELECTRE III and revised Simos’ procedure for group material selection under weighting uncertainty","year":2008,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Material Selection and Properties","field":"Materials Science","cited_by":125,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Rolls-Royce (Canada)","funders":"","keywords":"ELECTRE; Computer science; Selection (genetic algorithm); Weighting; Sample (material); Rank (graph theory); Multiple-criteria decision analysis; Process (computing); Task (project management); Function (biology); Reliability (semiconductor); Operations research; Artificial intelligence; Mathematics; Systems 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":[],"consensus_categories":[],"category_scores_codex":[0.0004455723,0.0002036525,0.0003738299,0.00009834112,0.0003302849,0.00008517592,0.0001238321,0.0001453253,0.00009783044],"category_scores_gemma":[0.00004448729,0.0001691966,0.00005377742,0.0002577939,0.00006178368,0.0001242636,0.00001736568,0.00005333626,0.00001856867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096338,"about_ca_system_score_gemma":0.0002533031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009391828,"about_ca_topic_score_gemma":0.0002043788,"domain_scores_codex":[0.9985049,0.0001568758,0.0005057186,0.0003912992,0.0001584882,0.0002826736],"domain_scores_gemma":[0.9991214,0.00006950069,0.0002871776,0.0001572657,0.0002628397,0.0001017871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006471561,0.00005548264,0.0002498364,0.0009539531,0.00001055755,1.285285e-7,0.0003222134,0.0003299997,0.9941378,0.001181519,0.001630219,0.0004811182],"study_design_scores_gemma":[0.002503412,0.0006255474,0.0003161047,0.0002887595,0.00005344102,0.00004238806,0.0001420028,0.03117479,0.941403,0.0001690202,0.02284386,0.0004377085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9252597,0.0006224919,0.07050464,0.00006935358,0.001005765,0.001805536,0.00002738382,0.0002510858,0.0004540487],"genre_scores_gemma":[0.997473,0.00001002231,0.000616825,0.00002580754,0.0006867544,0.0002891647,0.00004041805,0.00003089273,0.0008271286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0722133,"threshold_uncertainty_score":0.6899636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01876017246616747,"score_gpt":0.2453499232710192,"score_spread":0.2265897508048517,"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."}}