{"id":"W3120785167","doi":"10.1002/mcda.1730","title":"A disaggregation approach for indirect preference elicitation in Electre <scp>TRI‐nC</scp>: Application and validation","year":2021,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ELECTRE; Sorting; Pairwise comparison; Robustness (evolution); Preference; Preference elicitation; Computer science; Decision maker; Credibility; Operations research; Artificial intelligence; Mathematics; Statistics; Multiple-criteria decision analysis; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009632036,0.0003916164,0.001392483,0.004025868,0.0002673613,0.001200288,0.0008790305,0.0003119241,0.00007878652],"category_scores_gemma":[0.03380474,0.00032134,0.0006755397,0.006914938,0.00007732283,0.001501053,0.0001975396,0.0003723574,0.00001508743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002316286,"about_ca_system_score_gemma":0.0001869327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001995291,"about_ca_topic_score_gemma":0.0001490056,"domain_scores_codex":[0.9913598,0.001103633,0.00340653,0.001115697,0.002572966,0.0004413315],"domain_scores_gemma":[0.9847305,0.007983054,0.002534042,0.0009036523,0.003559698,0.0002890097],"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":[0.0005443043,0.00138314,0.07619374,0.00004836811,0.0006710314,0.00003935407,0.004124867,0.02177619,0.1011347,0.0002283981,0.00177054,0.7920854],"study_design_scores_gemma":[0.003914415,0.0001447873,0.1779666,0.0001132642,0.000670729,0.00008497257,0.002982429,0.7934248,0.01008347,0.008157899,0.002203978,0.0002526015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4616075,0.0004860032,0.537279,0.00008261736,0.0001547194,0.0003091514,0.00002923578,0.00001242507,0.00003940715],"genre_scores_gemma":[0.7929443,0.0001957381,0.2062831,0.00009975141,0.0001222485,0.00004614078,0.0000947864,0.00002790657,0.0001860699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7918328,"threshold_uncertainty_score":0.9999239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1688228980008006,"score_gpt":0.4272772082113002,"score_spread":0.2584543102104996,"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."}}