{"id":"W1972110671","doi":"10.1109/icrera.2014.7016455","title":"Prediction of the performance of a solar thermal energy system using adaptive neuro-fuzzy inference system","year":2014,"lang":"en","type":"article","venue":"2014 International Conference on Renewable Energy Research and Application (ICRERA)","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Adaptive neuro fuzzy inference system; Inference system; Solar energy; Neuro-fuzzy; Fuzzy inference system; Thermal; Computer science; Reliability (semiconductor); Inference; Fuzzy logic; Environmental science; Machine learning; Meteorology; Engineering; Fuzzy control system; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003158031,0.0004304104,0.0002936807,0.0001602638,0.0002113217,0.0003551296,0.0002460768,0.0003558804,0.0004091382],"category_scores_gemma":[0.0009883944,0.0001398702,0.0002008051,0.0001541504,0.0001570527,0.0003062489,0.0001323917,0.0002907834,0.0001126552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004405414,"about_ca_system_score_gemma":0.0003176199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01719301,"about_ca_topic_score_gemma":0.02089353,"domain_scores_codex":[0.9998755,0.00002576797,0.00000905758,0.00002389274,0.00005122086,0.00001443459],"domain_scores_gemma":[0.9997708,0.0001380567,0.00002395503,0.0000118909,0.00005048716,0.000004777974],"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":[0.0001183313,0.00006347165,0.005280079,0.00005766646,0.00002571844,0.00007573191,0.00006353015,0.9528235,0.01645419,0.0001629092,0.0001591229,0.02471577],"study_design_scores_gemma":[0.00000364369,0.00004744906,0.002893662,0.000002824815,0.000005903732,0.000008598246,0.00001490317,0.9932581,0.003626102,0.00006775692,0.00006649113,0.000004530832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9083419,0.0001552462,0.08815099,0.00006788971,0.00001886148,0.00004089908,0.0001081453,0.0002566188,0.002859507],"genre_scores_gemma":[0.9969231,0.00003589268,0.002702713,0.000002623896,0.000001275109,0.000008409351,0.00003279258,0.000002472126,0.0002905804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01719301,"threshold_uncertainty_score":0.03418589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05569274915310195,"score_gpt":0.2819028445130486,"score_spread":0.2262100953599467,"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."}}