{"id":"W4321385331","doi":"10.26868/29761662.2022.4","title":"MACHINE LEARNING FOR IMAGE-BASED RECOGNITION OF BUILDING AGE FOR URBAN ENERGY SIMULATION-TESTING AND VALIDATION ON AN EXEMPLARY CITY QUARTER","year":2022,"lang":"en","type":"article","venue":"Proceedings of BauSIM","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Quarter (Canadian coin); Artificial intelligence; Computer science; Process (computing); Machine learning; Artificial neural network; German; Energy (signal processing); Pattern recognition (psychology); Computer vision; Statistics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004611784,0.0006101577,0.0002844797,0.0005103961,0.0001404781,0.0003206102,0.0005114148,0.000459635,0.001450149],"category_scores_gemma":[0.0008281715,0.000183332,0.000407471,0.0004266729,0.0002144927,0.0002372545,0.0003331184,0.0002974657,0.0004298212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005461068,"about_ca_system_score_gemma":0.0003924997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01050947,"about_ca_topic_score_gemma":0.01118083,"domain_scores_codex":[0.9998312,0.00003402919,0.00001103177,0.00004980679,0.00003806798,0.00003587612],"domain_scores_gemma":[0.9997779,0.00006095348,0.00002092022,0.0000508411,0.00007079303,0.00001854135],"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":[0.0004309857,0.0003753695,0.02003724,0.0001278812,0.0001036639,0.0001353942,0.00006129454,0.8100316,0.02664571,0.000512279,0.001944547,0.1395939],"study_design_scores_gemma":[0.000006379199,0.00006166035,0.01005031,0.000008610667,0.000007678824,0.00001781354,0.00001915676,0.9774438,0.01183703,0.0001125831,0.0004287279,0.000006254502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431992,0.0001435116,0.05201761,0.00006369101,0.00003151262,0.00007068055,0.001009436,0.001136331,0.002327994],"genre_scores_gemma":[0.982326,0.00004280256,0.01563488,0.0000120684,0.000002532691,0.00004215898,0.001123066,0.0000276793,0.0007887622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01050947,"threshold_uncertainty_score":0.02089655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431927347697346,"score_gpt":0.2351959826154387,"score_spread":0.2108767091384652,"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."}}