{"id":"W2757920953","doi":"","title":"Using generative design principles to optimize the acoustic quality of a meeting room","year":2017,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Architecture and Computational Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reverberation; Computer science; Acoustics; Room acoustics; Soundproofing; Generative Design; Generative grammar; Human–computer interaction; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.003352818,0.001464807,0.0006062181,0.00130069,0.0006806565,0.003571666,0.001690696,0.001236404,0.006164678],"category_scores_gemma":[0.006474949,0.001076577,0.001577885,0.0006837757,0.001992238,0.002028636,0.003674045,0.001709625,0.001091944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008222,"about_ca_system_score_gemma":0.001107377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007013976,"about_ca_topic_score_gemma":0.001631135,"domain_scores_codex":[0.9981878,0.0006579693,0.00009637701,0.0002355053,0.0007013372,0.0001209921],"domain_scores_gemma":[0.9969208,0.001899913,0.0001961334,0.0005278586,0.0003625487,0.00009272953],"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":[0.0002543233,0.0003694437,0.004586658,0.001780608,0.0002941187,0.0006673554,0.003216419,0.5736644,0.08542285,0.1411618,0.002587234,0.1859948],"study_design_scores_gemma":[0.0002827662,0.001273287,0.002767009,0.0004397328,0.0004710303,0.001150176,0.001353013,0.7410624,0.04077486,0.1378494,0.07232368,0.0002527003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01712354,0.0003417431,0.9725562,0.0001705742,0.00004428661,0.0002382639,0.00004119538,0.0003791618,0.009105174],"genre_scores_gemma":[0.240561,0.0005600782,0.7529116,0.0001667679,0.00002289063,0.0006893023,0.0001277565,0.0004519061,0.004508725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006164678,"threshold_uncertainty_score":0.02062285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229705398222915,"score_gpt":0.301905759635276,"score_spread":0.1789352198129845,"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."}}