{"id":"W1979793758","doi":"10.1016/j.enbuild.2009.10.011","title":"Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers","year":2009,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Duct (anatomy); Nusselt number; Artificial neural network; Heat transfer; Heat exchanger; Airflow; Thermal conduction; Thermal; Convection; Forced convection; Mechanics; Engineering; Meteorology; Mechanical engineering; Computer science; Thermodynamics; Physics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001840276,0.0001336833,0.0001650855,0.000103219,0.0001466769,0.00001538369,0.00005022061,0.0001218954,0.00001615687],"category_scores_gemma":[0.000007531847,0.0001400515,0.00005740743,0.0002056625,0.00001773967,0.0001109872,0.00000940423,0.0000478226,5.973342e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002716991,"about_ca_system_score_gemma":0.00001610106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008524791,"about_ca_topic_score_gemma":0.00001437084,"domain_scores_codex":[0.9991924,0.00001250342,0.0002925492,0.0001668482,0.0001278124,0.0002078858],"domain_scores_gemma":[0.9997167,0.00004544059,0.00003539167,0.00007597864,0.00007591709,0.00005059817],"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":[0.00007847136,0.00003119769,0.0002079982,0.00001321839,0.0000200228,2.299042e-7,0.00007301537,0.9541513,0.0185893,0.0003652371,0.00001723155,0.02645278],"study_design_scores_gemma":[0.0003510528,0.0000758745,0.003124005,0.00002587359,0.000007760554,9.005799e-7,0.000004982905,0.9121913,0.08240097,0.00007141259,0.001609675,0.0001361926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5054333,0.00006399888,0.4941548,0.000009354501,0.0002029177,0.00005540208,0.000005180748,0.00005570725,0.00001936092],"genre_scores_gemma":[0.9553173,0.000003923889,0.04428546,0.00009828321,0.0001755467,0.00001694989,0.00006654558,0.00001637321,0.00001956972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4498841,"threshold_uncertainty_score":0.5711133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128040427463368,"score_gpt":0.2352620235018645,"score_spread":0.2224579807555277,"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."}}