{"id":"W4388573595","doi":"10.1016/j.fss.2023.108771","title":"Using I-subgroup-based weighted generalized interval t-norms for aggregating basic uncertain information","year":2023,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Science and Technology Program of Hunan Province; National Natural Science Foundation of China","keywords":"Mathematics; Interval (graph theory); Semigroup; Operator (biology); Function (biology); Construct (python library); Fuzzy set; Fuzzy logic; Discrete mathematics; Algebra over a field; Pure mathematics; Combinatorics; Artificial intelligence; Computer science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005299422,0.000242886,0.0005345066,0.0007717863,0.0004490757,0.001564922,0.0004586975,0.0001457262,0.00003986346],"category_scores_gemma":[0.002094008,0.0001692624,0.0001570682,0.001124649,0.00005411842,0.000965396,0.0001484159,0.0001054209,0.0001395121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008098001,"about_ca_system_score_gemma":0.00008426796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002544598,"about_ca_topic_score_gemma":0.00002731355,"domain_scores_codex":[0.9960847,0.0003290724,0.001438985,0.0004492857,0.001247898,0.0004500396],"domain_scores_gemma":[0.9964134,0.00169577,0.0006755931,0.0005264234,0.0005240706,0.0001647531],"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":[0.001289665,0.000134337,0.02831426,0.001393167,0.0002002452,0.0001369538,0.01518285,0.08135616,0.01830439,0.01335329,0.08015003,0.7601846],"study_design_scores_gemma":[0.001465791,0.00005383719,0.0008953321,0.0002290828,0.00001188195,0.00002059028,0.001748809,0.9740436,0.00008578436,0.002703877,0.01849486,0.0002465397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358082,0.0001405289,0.05970833,0.0002368832,0.002442486,0.0009459549,0.0001916658,0.0001710283,0.0003549625],"genre_scores_gemma":[0.9898371,0.000003703479,0.009188489,0.0002458614,0.0002301859,0.00007817782,0.0001100991,0.00002507984,0.0002812717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8926874,"threshold_uncertainty_score":0.9994715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2802906196046686,"score_gpt":0.4334019960240034,"score_spread":0.1531113764193348,"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."}}