{"id":"W4410557737","doi":"10.5194/icuc12-542","title":"Leveraging large language models to enhance urban building energy modeling: A case study","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Energy modeling; Architectural engineering; Energy (signal processing); Engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002057473,0.0006993568,0.0002621996,0.0005968707,0.0006164869,0.001341887,0.001289242,0.0007282763,0.002426924],"category_scores_gemma":[0.006559664,0.0002982233,0.0006970984,0.001237847,0.0007498043,0.001661275,0.00112868,0.0009511418,0.000549874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002247066,"about_ca_system_score_gemma":0.001777556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06954493,"about_ca_topic_score_gemma":0.1498679,"domain_scores_codex":[0.9989381,0.0006510273,0.00004433792,0.000134467,0.0001692067,0.00006281499],"domain_scores_gemma":[0.9960501,0.003049856,0.0001188662,0.0003730301,0.0003547681,0.00005342913],"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":[0.0003508246,0.0006516335,0.02386146,0.000676546,0.0001120669,0.003280765,0.003702987,0.754375,0.00910884,0.02864271,0.0122333,0.1630039],"study_design_scores_gemma":[0.00004269208,0.00005388232,0.002465754,0.00004555965,0.00003948895,0.0002026586,0.001081876,0.9584665,0.008114448,0.008538091,0.02090817,0.00004077302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5684301,0.0004024331,0.4054326,0.002598611,0.00006826157,0.0005854755,0.0048789,0.004173033,0.01343061],"genre_scores_gemma":[0.7045651,0.0002542806,0.2870458,0.000179307,0.00001661088,0.000267893,0.003607767,0.0004452444,0.003617889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06954493,"threshold_uncertainty_score":0.1382803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052752009676435,"score_gpt":0.2840148093585499,"score_spread":0.2634872892617856,"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."}}