{"id":"W1139905439","doi":"10.1016/j.renene.2015.08.028","title":"Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system","year":2015,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada; Australian Government","keywords":"Adaptive neuro fuzzy inference system; Inference system; Neuro-fuzzy; Artificial neural network; Computer science; Range (aeronautics); Renewable energy; Flexibility (engineering); Solar energy; Machine learning; Fuzzy logic; Engineering; Artificial intelligence; Fuzzy control system; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000354142,0.0001822829,0.0002664592,0.0001440573,0.0001322069,0.00007511609,0.0005479704,0.0001221683,6.176176e-7],"category_scores_gemma":[0.00002281809,0.0001748256,0.00008223884,0.0003657854,0.0000241359,0.0006051083,0.00009773956,0.00005598862,0.000001743852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000172785,"about_ca_system_score_gemma":0.0002792889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004337004,"about_ca_topic_score_gemma":0.00004487698,"domain_scores_codex":[0.9984572,0.0001114724,0.0003947883,0.0003644518,0.0003702225,0.0003018785],"domain_scores_gemma":[0.9985529,0.0001376894,0.0002590743,0.000501218,0.0003759625,0.0001732082],"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":[0.00003298939,0.00002015172,0.0001027101,0.00006938639,0.00002031963,0.000001420021,0.0002007129,0.9421173,0.001071572,0.05513458,0.0001988242,0.001030013],"study_design_scores_gemma":[0.0004546043,0.0002461648,0.00001417702,0.00009695594,0.00001263297,0.00001208763,0.0001409942,0.956987,0.03785753,0.0002056149,0.003830146,0.0001420404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008259704,0.0002113491,0.9856224,0.00001011262,0.000841908,0.00009241988,0.00001965188,0.0002567271,0.004685695],"genre_scores_gemma":[0.9891233,0.00003242963,0.01014317,0.0000492222,0.0001904169,0.00007643013,0.00001611612,0.00002088775,0.0003480527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9808636,"threshold_uncertainty_score":0.7129183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04017814854903167,"score_gpt":0.2115550154753852,"score_spread":0.1713768669263536,"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."}}