{"id":"W2182019509","doi":"","title":"Comparison between multiple linear regressions and artificial neural networks to predict urban sound quality","year":2010,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Noise Effects and Management","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"NeuroDevNet","funders":"","keywords":"Soundscape; Artificial neural network; Active listening; Computer science; Sound quality; Set (abstract data type); Linear regression; Quality (philosophy); Artificial intelligence; Machine learning; Statistics; Sound (geography); Psychology; Mathematics; Speech recognition; Acoustics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01654238,0.002160451,0.002129059,0.00221343,0.000419841,0.001575755,0.001944719,0.00143368,0.00376426],"category_scores_gemma":[0.04176121,0.0005384322,0.001717695,0.002275801,0.0004287053,0.002170744,0.0009198007,0.002270539,0.0008974031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173591,"about_ca_system_score_gemma":0.001047947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110187,"about_ca_topic_score_gemma":0.008130352,"domain_scores_codex":[0.9947947,0.003632226,0.0002440418,0.0007486253,0.0004154382,0.0001650421],"domain_scores_gemma":[0.904287,0.08962765,0.001411748,0.001389276,0.002816485,0.0004678482],"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":[0.01201801,0.00163375,0.1328607,0.0007322821,0.004055766,0.0001812916,0.0002556164,0.5557418,0.002409125,0.001991448,0.00341706,0.2847031],"study_design_scores_gemma":[0.0001575242,0.0009153246,0.02194116,0.00005203928,0.000498966,0.00003938477,0.0001287157,0.9735041,0.001060126,0.001303613,0.0003642146,0.00003482432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8458635,0.007811793,0.1376745,0.001377456,0.0008416902,0.0001720827,0.001421173,0.0008394412,0.00399849],"genre_scores_gemma":[0.9672126,0.001572777,0.02679502,0.0001129202,0.0002535128,0.0001302309,0.001008667,0.0001913618,0.002722862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01654238,"threshold_uncertainty_score":0.08748549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07797383401560551,"score_gpt":0.3921646286599785,"score_spread":0.3141907946443729,"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."}}