{"id":"W4297830713","doi":"10.1177/00375497221115734","title":"The role of latent representations for design space exploration of floorplans","year":2022,"lang":"en","type":"article","venue":"SIMULATION","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; York University; University Health Network","funders":"National Science Foundation","keywords":"Representation (politics); Computer science; Autoencoder; Space (punctuation); Graph; Visibility; Theoretical computer science; Semantics (computer science); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Deep learning","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":[0.0001438313,0.00003400581,0.00005990979,0.00004404173,0.00009714303,0.000005095386,0.00004655668,0.00001084478,0.00002026246],"category_scores_gemma":[0.00002708712,0.00002986667,0.0000431519,0.0001487809,0.000007624095,0.0000752225,0.000007360244,0.00002468492,6.536316e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002024727,"about_ca_system_score_gemma":0.00000703577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002802645,"about_ca_topic_score_gemma":0.00001412438,"domain_scores_codex":[0.9996098,0.00003717372,0.0001502097,0.00004645411,0.0001068431,0.0000495194],"domain_scores_gemma":[0.9995854,0.00021362,0.00004763932,0.00009724566,0.00004732218,0.000008734239],"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":[0.00002190585,0.000008093379,0.0003090562,0.000004352114,0.00002672248,2.434011e-8,0.0005684481,0.9843782,0.01096714,0.0009922723,0.00009009407,0.002633645],"study_design_scores_gemma":[0.0001053895,0.00003556646,0.0002563477,0.000001123485,0.00002767721,3.548945e-8,0.0002407008,0.9826888,0.01096295,0.004781406,0.0008698737,0.00003017017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02182968,0.0001810615,0.977347,0.0000611051,0.00006643827,0.0002986469,0.00001831906,0.00003305961,0.0001647098],"genre_scores_gemma":[0.9985046,0.000007873241,0.001259763,0.000001344151,0.00001595604,0.0000564016,0.00003073828,0.000007168247,0.0001162024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9766749,"threshold_uncertainty_score":0.1217928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03601752378309234,"score_gpt":0.2480839210572297,"score_spread":0.2120663972741373,"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."}}