{"id":"W2903643449","doi":"10.1007/s12053-018-9757-y","title":"Performance of heat transport systems: least square method generated correlations of non-dimensional variables","year":2018,"lang":"en","type":"article","venue":"Energy Efficiency","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Hydro-Québec","keywords":"Exergy efficiency; Exergy; Process engineering; Heating system; Waste heat; Efficient energy use; Environmental science; Mathematical optimization; Computer science; Mathematics; Mechanical engineering; Engineering; Heat exchanger","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290004,0.0004749152,0.000567512,0.0004139334,0.0003545585,0.0006135458,0.0003803895,0.0005089321,0.0008218361],"category_scores_gemma":[0.003266389,0.0002398236,0.0004409138,0.000598495,0.0003802773,0.0007063975,0.0003010744,0.0004848061,0.0002324555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004735125,"about_ca_system_score_gemma":0.0006934798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005134123,"about_ca_topic_score_gemma":0.003327655,"domain_scores_codex":[0.9996213,0.0001929648,0.00001904327,0.00005699233,0.00007944358,0.00003018198],"domain_scores_gemma":[0.9973868,0.001961791,0.0001382958,0.0001411407,0.000320462,0.00005157701],"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":[0.0006642989,0.0001344697,0.004765103,0.0001014764,0.00008043051,0.00004088888,0.00007708902,0.9657611,0.01261443,0.0007603625,0.0003028349,0.01469736],"study_design_scores_gemma":[0.0000058193,0.00008878948,0.001148058,0.000001185402,0.000005485826,0.000004125797,0.000007548862,0.9944341,0.004202314,0.00006924388,0.00002800279,0.000005360372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.938819,0.00018809,0.05880852,0.00007031059,0.00001993884,0.00002286232,0.0002102531,0.0006037104,0.001257323],"genre_scores_gemma":[0.99518,0.00002334913,0.004303896,0.000005186472,0.00000331454,0.0000134713,0.0001476253,0.00004186135,0.0002813379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005134123,"threshold_uncertainty_score":0.01020849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005643856115474857,"score_gpt":0.2121155421147289,"score_spread":0.2064716859992541,"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."}}