{"id":"W3096060094","doi":"10.1016/j.renene.2020.10.074","title":"A successive flux estimation method for rapid g-function construction of small to large-scale ground heat exchanger","year":2020,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Geothermal Energy Systems and Applications","field":"Energy","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Natural Resources Canada","funders":"Office of Energy Research and Development","keywords":"Convolution (computer science); Heat exchanger; Transfer function; Computation; Function (biology); Applied mathematics; Convergence (economics); Flux (metallurgy); Algorithm; Mathematical optimization; Heat flux; Mathematics; Computer science; Heat transfer; Mechanics; Physics; Engineering; Chemistry; Thermodynamics","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":[0.0001834353,0.0002066689,0.0003577459,0.0001122962,0.0001575122,0.0000359395,0.0001568043,0.0001539424,0.0003283801],"category_scores_gemma":[0.00006104487,0.0002047433,0.0001415894,0.0004962886,0.00002408998,0.0001330601,0.00004839073,0.00004452468,0.00001429864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005408371,"about_ca_system_score_gemma":0.00005145225,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04302081,"about_ca_topic_score_gemma":0.004758229,"domain_scores_codex":[0.9985553,0.00009592054,0.0004328885,0.0004356854,0.0001696628,0.0003105261],"domain_scores_gemma":[0.999025,0.0001265176,0.0001644995,0.0002852261,0.0002047914,0.0001939217],"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":[0.0001137014,0.00004553357,0.0000110286,0.00007254793,0.00005665174,2.956971e-7,0.0003080999,0.8880244,0.05965021,0.03372724,0.000562977,0.01742733],"study_design_scores_gemma":[0.0008820607,0.000281131,0.0000734885,0.00004307399,0.00007922074,0.000004882665,0.0004890458,0.630171,0.08142024,0.002567717,0.2837131,0.0002750087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008618203,0.0001523504,0.9852061,0.000735121,0.0002541094,0.0002360503,0.00004636329,0.0001237805,0.004627982],"genre_scores_gemma":[0.7604887,0.00002846943,0.2278186,0.002133619,0.001213945,0.001406561,0.0006554403,0.0001370409,0.006117594],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7573874,"threshold_uncertainty_score":0.9633518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02008560527243032,"score_gpt":0.2501221927843553,"score_spread":0.230036587511925,"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."}}