{"id":"W2908559044","doi":"10.1007/s12559-019-9622-0","title":"Group Decision-Making with Linguistic Cognition from a Reliability Perspective","year":2019,"lang":"en","type":"article","venue":"Cognitive Computation","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Cognition; Reliability (semiconductor); Computer science; Consistency (knowledge bases); Artificial intelligence; Ranking (information retrieval); Perspective (graphical); Machine learning; Natural language processing; Cognitive psychology; Psychology","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","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002030116,0.0003431098,0.0005548153,0.0005604959,0.0002732786,0.0006947241,0.0003586019,0.0001405012,0.001282397],"category_scores_gemma":[0.02796528,0.0002695939,0.0001600348,0.001295203,0.0001851424,0.0006550184,0.0002398942,0.0003379762,0.002795707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002263354,"about_ca_system_score_gemma":0.0001242168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005421427,"about_ca_topic_score_gemma":0.0000615328,"domain_scores_codex":[0.9942176,0.0006087253,0.0009425032,0.001580157,0.002291475,0.0003595629],"domain_scores_gemma":[0.9683831,0.02482743,0.0006676728,0.0003325633,0.005644154,0.0001450971],"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":[0.004209887,0.0005416448,0.04141715,0.00001552874,0.0001322368,0.0001358513,0.006474186,0.001424709,0.0008145449,0.001471404,0.0003382603,0.9430246],"study_design_scores_gemma":[0.003105684,0.000446666,0.2543471,0.001143389,0.0001056034,0.00002047613,0.007908984,0.1982401,0.0001058999,0.5338075,0.0001591104,0.0006095702],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4924065,0.00005801237,0.5021221,0.00003642689,0.0005686154,0.0005688913,0.00007369,0.00007999885,0.004085718],"genre_scores_gemma":[0.9678709,0.00000199453,0.03134757,0.0003762658,0.0002273133,0.00003024133,0.00006758266,0.00003991099,0.00003821325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9424151,"threshold_uncertainty_score":0.9999756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06248454863765145,"score_gpt":0.4134187554473731,"score_spread":0.3509342068097216,"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."}}