{"id":"W4377018026","doi":"10.1016/j.apenergy.2023.121228","title":"Extracting principal building variables from automatically collected urban scale façade images for energy conservation through deep transfer learning","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Principal (computer security); Scale (ratio); Architectural engineering; Efficient energy use; Computer science; Building model; Facade; Energy conservation; Civil engineering; Engineering; Simulation; Geography; Cartography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001344274,0.0002704053,0.0002953769,0.0001267778,0.0003230373,0.0001007239,0.0001809225,0.0002433046,0.00005409411],"category_scores_gemma":[0.00003391273,0.0003054032,0.00007913032,0.0006687068,0.00003380288,0.0002125067,0.00003262195,0.0001531857,0.000002222635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008180557,"about_ca_system_score_gemma":0.00003277816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003283853,"about_ca_topic_score_gemma":0.0001225265,"domain_scores_codex":[0.9985454,0.00003038898,0.0004087311,0.0003528261,0.0001994681,0.000463165],"domain_scores_gemma":[0.9991741,0.0004615991,0.0000481683,0.000193292,0.00005293865,0.00006996856],"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":[0.00002810444,0.000019131,0.00004280655,0.00002886457,0.00009346922,0.000001642287,0.0002631518,0.8656678,0.06141951,0.0672974,0.0009576571,0.004180444],"study_design_scores_gemma":[0.0006133309,0.00002180786,0.0001407745,0.00003795588,0.00005249371,0.000001428381,0.000102571,0.8824235,0.08731931,0.004806148,0.02411253,0.0003681792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09894723,0.00007899248,0.8940197,0.00004794609,0.0002374304,0.00009344415,0.00001156712,0.001827449,0.004736205],"genre_scores_gemma":[0.9397475,0.0001232795,0.05822708,0.0001358368,0.0002474288,0.0003981853,0.0005347897,0.0001179217,0.0004679837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8408003,"threshold_uncertainty_score":0.9999398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00920123706606707,"score_gpt":0.2051457499963056,"score_spread":0.1959445129302385,"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."}}