{"id":"W1995986371","doi":"10.1016/j.ejor.2004.01.039","title":"A simple method for computation of fuzzy linear regression","year":2004,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":171,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; University of Saskatchewan","funders":"","keywords":"Simple (philosophy); Fuzzy logic; Computation; Variable (mathematics); Simple linear regression; Mathematics; Computer science; Mathematical optimization; Regression; Regression analysis; Algorithm; Artificial intelligence; Statistics","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":[0.001177334,0.000965386,0.00125359,0.001246928,0.0008438363,0.0009602394,0.001336153,0.001003904,0.01073142],"category_scores_gemma":[0.005699618,0.0006000695,0.001205499,0.001229655,0.0005485203,0.0009422159,0.001242606,0.001557086,0.004086187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000387429,"about_ca_system_score_gemma":0.0008224894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650842,"about_ca_topic_score_gemma":0.002601694,"domain_scores_codex":[0.9990535,0.0002339496,0.00006433514,0.0001572529,0.0004440231,0.00004692626],"domain_scores_gemma":[0.9986026,0.0006169717,0.00006004825,0.0002698526,0.0003936951,0.00005692937],"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":[0.0002394687,0.00009580946,0.000463862,0.0003586551,0.0001643426,0.0002325668,0.0001741609,0.04279163,0.04969999,0.08646255,0.00921721,0.8100997],"study_design_scores_gemma":[0.0001646742,0.0002186086,0.001135988,0.000060015,0.0001618397,0.0007316059,0.0000485309,0.8034006,0.03606191,0.1049505,0.05291694,0.0001487512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004099342,0.00003781535,0.9986896,0.00001691395,0.00004290949,0.00001656468,0.00002601682,0.0004289828,0.0003312152],"genre_scores_gemma":[0.01744389,0.00007435701,0.9790813,0.00004498902,0.00006103087,0.0001264705,0.00009052041,0.0002396226,0.00283778],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01073142,"threshold_uncertainty_score":0.03590024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2072447309299977,"score_gpt":0.4825660956785808,"score_spread":0.275321364748583,"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."}}