{"id":"W2980521232","doi":"10.3233/jifs-191092","title":"Clustering experts in linguistic environment: A hybrid method","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Tuple; Artificial intelligence; Data mining; Document clustering; Machine learning; Hierarchical clustering; Natural language processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003087723,0.001190967,0.001450453,0.004181913,0.001106393,0.002058893,0.002308047,0.001806182,0.002985637],"category_scores_gemma":[0.00512707,0.0007192036,0.001658886,0.003361027,0.0008113064,0.001857885,0.001996391,0.0009468172,0.0009278184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009971111,"about_ca_system_score_gemma":0.001512608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003862307,"about_ca_topic_score_gemma":0.0036446,"domain_scores_codex":[0.9965537,0.001411965,0.0001945766,0.0007792917,0.0008348812,0.0002255287],"domain_scores_gemma":[0.9976937,0.001219035,0.000165886,0.0001596794,0.0006539812,0.0001076312],"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":[0.0003240212,0.0002853027,0.00329246,0.000384746,0.0004155988,0.0002831122,0.001225817,0.4669943,0.009681432,0.03045722,0.002830256,0.4838258],"study_design_scores_gemma":[0.00001973024,0.00004865912,0.0003028819,0.00001577436,0.00003566315,0.00005774844,0.0001053855,0.9918826,0.0009712512,0.005326745,0.001208432,0.00002515292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00570172,0.00008602856,0.9930757,0.00006450379,0.00001672469,0.00006591366,0.00002546035,0.0001649014,0.0007990944],"genre_scores_gemma":[0.1603162,0.0001798539,0.8362895,0.0001069267,0.00008130988,0.0003375148,0.0002244565,0.00008824548,0.002376136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004181913,"threshold_uncertainty_score":0.01632965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1222279380744868,"score_gpt":0.4129589976182834,"score_spread":0.2907310595437966,"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."}}