{"id":"W4415053970","doi":"10.1016/j.rineng.2025.107599","title":"Harnessing machine learning for rapid and cost-efficient 3D geometry generation in neighborhood energy modeling","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Energy modeling; Memory footprint; Footprint; Segmentation; Efficient energy use; 3D city models; Image processing; Artificial neural network; Ranging","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":[0.000481059,0.0007446919,0.0005551438,0.001055788,0.0003252494,0.001267419,0.00148248,0.000695201,0.003007893],"category_scores_gemma":[0.0018355,0.0005829703,0.0009461485,0.000982306,0.0004984772,0.001792243,0.001177221,0.0008279447,0.001226668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009013157,"about_ca_system_score_gemma":0.0008791852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052051,"about_ca_topic_score_gemma":0.01668634,"domain_scores_codex":[0.9996362,0.00005252241,0.00001830933,0.0001020131,0.0001575045,0.00003342535],"domain_scores_gemma":[0.9995885,0.0001284322,0.00003127535,0.0001232852,0.0001103241,0.00001822143],"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":[0.00002817974,0.0000464913,0.001774353,0.00006866855,0.00004190864,0.00007242345,0.00006588201,0.8683765,0.004788681,0.006425685,0.002272363,0.1160389],"study_design_scores_gemma":[0.000001657054,0.000002861099,0.000133701,0.000002907871,0.000002046985,0.000007487745,0.000008556583,0.9956564,0.001344453,0.002082762,0.000753599,0.000003577936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01875345,0.00009042462,0.9742173,0.0001228057,0.00003316994,0.00003666053,0.0004101384,0.003848212,0.002487836],"genre_scores_gemma":[0.4689509,0.0001879434,0.5256755,0.0001374297,0.00003430135,0.0001545606,0.002037084,0.0009432232,0.001878996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01052051,"threshold_uncertainty_score":0.02091855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399205476727228,"score_gpt":0.2304916364307102,"score_spread":0.216499581663438,"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."}}