{"id":"W1524874296","doi":"10.1007/s11518-013-5213-x","title":"A novel approach to characterizing hesitations in intuitionistic fuzzy numbers","year":2013,"lang":"en","type":"article","venue":"Journal of Systems Science and Systems Engineering","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Operationalization; Novelty; Operator (biology); Fuzzy logic; Mathematics; Computer science; Computational intelligence; Artificial intelligence; Management science; Algebra over a field; Operations research; Mathematical optimization; Engineering; Epistemology; Pure mathematics; Psychology; Social psychology; Philosophy","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0107536,0.0001853908,0.0006017267,0.002152173,0.000200318,0.00279213,0.0009460923,0.00006732871,0.000004254058],"category_scores_gemma":[0.00534131,0.0001379642,0.00006288436,0.002529958,0.00007973615,0.002167276,0.0001321162,0.0002283393,0.00005404418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792826,"about_ca_system_score_gemma":0.0001846244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002120321,"about_ca_topic_score_gemma":0.000002000794,"domain_scores_codex":[0.9946794,0.00008475553,0.001840209,0.0004041821,0.002562399,0.0004290531],"domain_scores_gemma":[0.9962919,0.0007237137,0.0006074614,0.0003596037,0.001604101,0.0004132269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001475926,0.0001902259,0.004678677,0.000229419,0.0000277971,0.00003389257,0.008546956,0.2512272,0.7188637,0.01190877,0.001350958,0.002927643],"study_design_scores_gemma":[0.0008289536,0.0001453302,0.08478165,0.001686787,0.00001177959,0.002474966,0.01611467,0.8898554,0.000103979,0.0001316808,0.003333186,0.0005315523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.792021,0.0002332276,0.2031253,0.0001681471,0.002790383,0.0005970423,0.000005003257,0.00001930245,0.001040523],"genre_scores_gemma":[0.9923314,0.000002890034,0.007248767,0.00003475218,0.0002325542,0.00003829529,1.694058e-7,0.00001355257,0.00009757713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7187598,"threshold_uncertainty_score":0.9982431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1118566325325302,"score_gpt":0.3424351018478576,"score_spread":0.2305784693153274,"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."}}