{"id":"W4414591283","doi":"10.1080/23311916.2025.2558767","title":"Optimizing neural network architectures for ground temperature prediction in ground source heat pump systems","year":2025,"lang":"en","type":"article","venue":"Cogent Engineering","topic":"Geothermal Energy Systems and Applications","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Sizing; Heat pump; Artificial neural network; Borehole; Heat exchanger; Work (physics); Thermal; Heat transfer","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001395921,0.0001933717,0.0002218752,0.0001263701,0.0001203908,0.00009830719,0.0001414925,0.0001258594,0.000004822948],"category_scores_gemma":[0.00001792624,0.0001900683,0.00008299308,0.0003222074,0.00001020487,0.00003534894,0.0000339403,0.0001672933,0.000002032179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001017354,"about_ca_system_score_gemma":0.00001453667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001095783,"about_ca_topic_score_gemma":0.0001318032,"domain_scores_codex":[0.9989453,0.00002245609,0.0003125579,0.0002723872,0.00009280175,0.0003545126],"domain_scores_gemma":[0.9995674,0.00008779991,0.00002946236,0.0002299452,0.00003018446,0.00005513385],"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":[0.00001085731,0.00001317809,0.0001487087,0.0001382896,0.00004277399,0.000001057674,0.0001125401,0.9841012,0.007145148,0.0078305,0.00009918863,0.0003565582],"study_design_scores_gemma":[0.0004244938,0.00001791462,0.001759547,0.0002611935,0.00002092077,0.00001011885,0.0001006992,0.9462459,0.0002772021,0.00004217229,0.05066779,0.0001720559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.954429,0.003076447,0.03941482,0.0001483031,0.001336956,0.0006510858,0.00001300847,0.000326907,0.0006034143],"genre_scores_gemma":[0.9966177,0.00000603347,0.0003526322,0.00005201232,0.0006350241,0.0006132075,0.00003922781,0.00004012565,0.001644015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0505686,"threshold_uncertainty_score":0.7750759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007394528695865661,"score_gpt":0.201722910810053,"score_spread":0.1943283821141874,"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."}}