{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009915921,0.0004440363,0.0008005131,0.00020925,0.001597291,0.000182181,0.0009637405,0.0007451954,0.0001386441],"category_scores_gemma":[0.003504344,0.0004209528,0.0001696687,0.0002860181,0.0002305336,0.00009512496,0.003721358,0.002993498,0.00005195842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119011,"about_ca_system_score_gemma":0.0001733533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002651144,"about_ca_topic_score_gemma":0.009229137,"domain_scores_codex":[0.9851831,0.01140626,0.001200609,0.001020013,0.0004849136,0.0007050815],"domain_scores_gemma":[0.9892504,0.005776544,0.0008985383,0.002327482,0.001193917,0.0005531221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001543236,0.001038751,0.8231395,0.001274864,0.0002770245,0.00000719214,0.03329616,0.001528015,0.001246233,0.03291347,0.03006552,0.0750589],"study_design_scores_gemma":[0.002226364,0.000005623541,0.5171292,0.005973516,0.0004027709,0.000001396338,0.001332921,0.3750334,0.001729508,0.005022361,0.08917774,0.001965165],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7875842,0.0003517299,0.1894691,0.01154986,0.001446457,0.002735164,0.0002131291,0.0004233493,0.006227025],"genre_scores_gemma":[0.9817356,0.00005961048,0.01245215,0.0003135072,0.0004770777,0.0003267322,0.0008863466,0.00007056213,0.003678364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3735054,"threshold_uncertainty_score":0.9998242,"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."}}