{"id":"W2887697969","doi":"10.25643/bauhaus-universitaet.3270","title":"A numerical comparison of the impact of different climatic conditions in different geographic locations on the construction of an office building","year":2017,"lang":"en","type":"article","venue":"Publication Server of Weimar Bauhaus-University (Weimar Bauhaus-University)","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Architectural engineering; Scope (computer science); Efficient energy use; Computer science; Work (physics); Set (abstract data type); Environmental science; Variance (accounting); Civil engineering; Meteorology; Geography; Engineering; Business; Mechanical engineering","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":[0.0003879507,0.0002561168,0.0003925337,0.0004897544,0.0004705873,0.0009256428,0.0004424953,0.0006506987,0.003922373],"category_scores_gemma":[0.001606359,0.0001705297,0.0005556838,0.0007125472,0.0004518099,0.0004979406,0.0005416065,0.000493241,0.0001869538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004127328,"about_ca_system_score_gemma":0.0003303413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007284942,"about_ca_topic_score_gemma":0.00637329,"domain_scores_codex":[0.999819,0.00004454471,0.000009630631,0.00003073964,0.00004079548,0.00005530398],"domain_scores_gemma":[0.9988945,0.0007645237,0.0001018479,0.00007306533,0.000102498,0.0000636057],"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":[0.000332437,0.000259285,0.03013287,0.0002092079,0.00005233954,0.000437115,0.0002696608,0.9477848,0.006112752,0.002798156,0.0009296332,0.01068186],"study_design_scores_gemma":[0.00002810266,0.0004359723,0.02421188,0.00002511622,0.00004019597,0.00009315163,0.0008308494,0.9693927,0.003167851,0.0007266958,0.001015276,0.00003222188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889242,0.00009233085,0.003840629,0.00007028228,0.00003014242,0.00001633245,0.0002857145,0.00005693918,0.006683317],"genre_scores_gemma":[0.9973617,0.00005658982,0.001892742,0.000006707719,0.000003264399,0.00001026222,0.0001243101,0.000009086811,0.0005353665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007284942,"threshold_uncertainty_score":0.01448506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550567358174557,"score_gpt":0.2352612040331075,"score_spread":0.219755530451362,"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."}}