{"id":"W2954141911","doi":"10.3233/jifs-181297","title":"Fuzzy demographic analysis using fuzzy regression models based on fuzzy distance–A case on the impact of fuzzy demographic factors on monetary aggregates in Canada versus Japan","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fuzzy logic; Fuzzy number; Fuzzy set; Mathematics; Computer science; Ambiguity; Type-2 fuzzy sets and systems; Regression analysis; Econometrics; Fuzzy set operations; Regression; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001739819,0.0003943707,0.0004735231,0.00134535,0.00113383,0.001714454,0.001072775,0.0006660152,0.001697664],"category_scores_gemma":[0.004095234,0.0001656697,0.0009572505,0.001353295,0.0008402046,0.0006451412,0.0005738602,0.0005809488,0.0001248049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006721276,"about_ca_system_score_gemma":0.003715248,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6905056,"about_ca_topic_score_gemma":0.4425336,"domain_scores_codex":[0.9993919,0.0001870496,0.00002605001,0.00008256797,0.0001644931,0.0001478004],"domain_scores_gemma":[0.9988791,0.0004168101,0.000117504,0.00003704028,0.0004594624,0.00008996278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001737351,0.0001105727,0.04921779,0.0001144475,0.0000985076,0.001608061,0.001595739,0.8394732,0.0009748692,0.07073877,0.002332022,0.03356233],"study_design_scores_gemma":[0.000008146687,0.00002481269,0.01578645,0.00002425728,0.00004333997,0.0000881669,0.000894115,0.9752481,0.0002508443,0.005571212,0.002023906,0.00003670499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9144024,0.001091858,0.0605887,0.001252649,0.00006223212,0.000101259,0.0003810369,0.0001327079,0.02198709],"genre_scores_gemma":[0.9900552,0.0003075637,0.006608873,0.00002249271,0.000008416759,0.00001787485,0.00006960926,0.00000920575,0.002900786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3094944,"threshold_uncertainty_score":0.6226342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04793873090875127,"score_gpt":0.2893377749723213,"score_spread":0.24139904406357,"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."}}