{"id":"W4250291293","doi":"10.26868/25222708.2019.211232","title":"Adaptive Sampling For Building Simulation Surrogate Model Derivation Using The LOLA-Voronoi Algorithm","year":2020,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Canarie","keywords":"Voronoi diagram; Centroidal Voronoi tessellation; Computer science; Sampling (signal processing); Adaptive sampling; Algorithm; Surrogate model; Mathematical optimization; Mathematics; Statistics; Machine learning; Computer vision; Monte Carlo method; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001864276,0.0006455819,0.001212634,0.001095068,0.0005749689,0.0009565327,0.001651454,0.0009756678,0.00326453],"category_scores_gemma":[0.006801383,0.0007100418,0.00104776,0.001057689,0.0006112654,0.0009063653,0.001459491,0.001470329,0.0008369333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008614324,"about_ca_system_score_gemma":0.001465538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007038932,"about_ca_topic_score_gemma":0.006416537,"domain_scores_codex":[0.9992549,0.0003491296,0.0000316118,0.00006514328,0.0002435189,0.00005573291],"domain_scores_gemma":[0.9975256,0.001683474,0.0001134803,0.0001677741,0.0004313411,0.00007842819],"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":[0.00003764261,0.00002891214,0.0005417329,0.00005686468,0.00002108651,0.00004917568,0.00004922682,0.9502993,0.001191927,0.01904557,0.0005358744,0.02814276],"study_design_scores_gemma":[0.000002752171,0.000005558234,0.00001931983,0.000002833891,0.000001199854,0.000005325046,0.000002863686,0.9969949,0.0001912391,0.002403113,0.0003692078,0.000001805188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001846167,0.00004892108,0.9973623,0.00002205166,0.000006033241,0.00002070075,0.00001818293,0.000128977,0.0005466438],"genre_scores_gemma":[0.1876087,0.0002020798,0.8094605,0.00006193009,0.00002713199,0.0004098476,0.0003171369,0.000296053,0.001616596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007038932,"threshold_uncertainty_score":0.01399589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.437817657541963,"score_gpt":0.4699098035917947,"score_spread":0.03209214604983168,"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."}}