{"id":"W2030574188","doi":"10.1016/j.applthermaleng.2014.07.040","title":"Performance prediction of a solar thermal energy system using artificial neural networks","year":2014,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Artificial neural network; Heat exchanger; Solar water heating; Solar energy; Thermal; Engineering; Range (aeronautics); Test data; Meteorology; Environmental science; Algorithm; Computer science; Mechanical engineering; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003115709,0.0002837276,0.0003692124,0.00009645189,0.0001099975,0.00002903331,0.0002285192,0.0001902496,0.00003088906],"category_scores_gemma":[0.000007481877,0.0002659123,0.0001087447,0.0002227373,0.00003119636,0.0001229595,0.00005776789,0.0001910469,0.00000725297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006622704,"about_ca_system_score_gemma":0.00001036711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004995329,"about_ca_topic_score_gemma":0.000004076755,"domain_scores_codex":[0.99849,0.00004842037,0.0004990961,0.0002676936,0.0002488224,0.0004459335],"domain_scores_gemma":[0.9992558,0.00006621393,0.0002012003,0.0003408144,0.00003839203,0.00009756912],"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":[0.00004916116,0.0000119215,0.0001817462,0.00007575249,0.00004317187,9.0324e-7,0.0000776091,0.6237419,0.3568025,0.01053544,4.635756e-7,0.008479439],"study_design_scores_gemma":[0.0002831198,0.00004582106,0.001393031,0.00007114982,0.00003755285,0.00001729047,0.0000480848,0.9340521,0.06343125,0.000004545462,0.0004020254,0.0002141091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495893,0.00007365835,0.04405868,0.000001249932,0.0008212448,0.0001281295,0.000004455495,0.0003271083,0.004996176],"genre_scores_gemma":[0.9983487,0.000001535176,0.0001797848,0.00001679741,0.001295754,0.0000437285,0.00001554438,0.00008459193,0.00001357503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3103101,"threshold_uncertainty_score":0.9999793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024435654818803,"score_gpt":0.1656130875652409,"score_spread":0.1553687310170529,"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."}}