{"id":"W2118752654","doi":"10.1109/icsmc.2009.5346801","title":"The comparison of neural network and hybrid neuro-fuzzy based inferential sensor models for space heating systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Neuro-fuzzy; Artificial neural network; Computer science; Artificial intelligence; Inference; Fuzzy control system; Range (aeronautics); Soft sensor; Fuzzy logic; Machine learning; Engineering; Process (computing)","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":[],"consensus_categories":[],"category_scores_codex":[0.00007235423,0.00008391805,0.0001296702,0.00001693976,0.0001078837,0.00004721289,0.00005198934,0.00002748642,6.551526e-7],"category_scores_gemma":[0.00000783474,0.00006459712,0.00002815228,0.00005493687,0.00001070324,0.00006440323,0.000006446345,0.00005734992,3.348537e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006376268,"about_ca_system_score_gemma":0.000004173645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002069602,"about_ca_topic_score_gemma":0.000005911338,"domain_scores_codex":[0.9995146,0.00001600555,0.0001767425,0.00007782511,0.00006467185,0.0001501422],"domain_scores_gemma":[0.9996707,0.0001401117,0.00003407963,0.0001017206,0.00002706115,0.0000262859],"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":[0.00001401749,0.000005129123,0.0001186077,0.00001780006,0.00000675488,1.070602e-7,0.00001122847,0.9891287,0.0001257648,0.008638407,0.0006128648,0.001320631],"study_design_scores_gemma":[0.0001888331,0.00004652265,0.00004424166,0.00001634007,0.00001053427,0.000001388783,0.00001473316,0.9984038,0.0007684599,0.000304898,0.0001296193,0.0000706135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1842382,0.0003835657,0.8137172,0.0001065348,0.0003475638,0.0001886363,0.000002337287,0.0001654137,0.000850512],"genre_scores_gemma":[0.9953318,0.00001018868,0.004484915,0.00002799493,0.00008933731,0.000007582692,0.000007668505,0.00001168559,0.00002878206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8110936,"threshold_uncertainty_score":0.2634194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671152966155014,"score_gpt":0.2271091700662819,"score_spread":0.2103976404047317,"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."}}