{"id":"W3034449794","doi":"10.1016/j.buildenv.2020.106964","title":"Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey","year":2020,"lang":"en","type":"article","venue":"Building and Environment","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Lawrence Berkeley National Laboratory; Building Technologies Office; Bayer-Stiftungen; Energiteknologisk udviklings- og demonstrationsprogram; Energistyrelsen; National Natural Science Foundation of China; Australian Research Council; Alexander von Humboldt-Stiftung; U.S. Department of Energy; National Science Foundation","keywords":"Range (aeronautics); Computer science; Scale (ratio); Energy modeling; Urban design; Set (abstract data type); Neighbourhood (mathematics); Urban planning; Data science; Transport engineering; Efficient energy use; Architectural engineering; Civil engineering; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.000999874,0.0001117984,0.0001880415,0.00004847379,0.0002949206,0.00006520959,0.0001046179,0.00007862162,0.00007175679],"category_scores_gemma":[0.00003961353,0.0001175274,0.00003616509,0.0001319473,0.0002366362,0.00008571614,0.00007292812,0.0001039142,0.000002371781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008567033,"about_ca_system_score_gemma":0.00002511788,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04310647,"about_ca_topic_score_gemma":0.003811723,"domain_scores_codex":[0.9986718,0.0002276455,0.0002197468,0.0004198407,0.0002229016,0.0002381104],"domain_scores_gemma":[0.9994974,0.0001297504,0.00004762594,0.0001160087,0.000007149251,0.0002020589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003883425,0.0002338232,0.8838883,0.00002718493,0.00002267704,0.000004612863,0.01871465,0.07899979,0.000301944,0.001576814,0.0001250963,0.01606628],"study_design_scores_gemma":[0.001754593,0.0003434658,0.3628882,0.0001482756,0.0002279393,0.000001637368,0.01275074,0.560706,0.0006119939,0.002953639,0.05600511,0.001608307],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727222,0.0006582396,0.02556582,0.0008049606,0.00001479444,0.00006899673,0.000009804369,0.00002343125,0.0001317337],"genre_scores_gemma":[0.9977549,0.001123324,0.0007570729,0.0001962186,0.00005456612,0.00001748179,0.000007399034,0.000007818971,0.00008117075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.521,"threshold_uncertainty_score":0.9632656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03496753444508368,"score_gpt":0.258097039436264,"score_spread":0.2231295049911803,"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."}}