{"id":"W4382066226","doi":"10.3390/su151310020","title":"Optimizing Regression Models for Predicting Noise Pollution Caused by Road Traffic","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Noise Effects and Management","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Qassim University","keywords":"Mean squared error; Noise (video); Kriging; Hyperparameter; Regression; Regression analysis; Support vector machine; Noise pollution; Computer science; Pollution; Statistics; Predictive modelling; Mathematics; Machine learning; Artificial intelligence; Noise reduction","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":[0.001830372,0.00133279,0.0008371781,0.0008701813,0.0002824662,0.0009210619,0.0008626776,0.001077172,0.0007364962],"category_scores_gemma":[0.005392641,0.0004626446,0.001018472,0.001343312,0.0003021939,0.0007561611,0.0005701562,0.00108499,0.0003548796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006789726,"about_ca_system_score_gemma":0.001260631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01747347,"about_ca_topic_score_gemma":0.0122829,"domain_scores_codex":[0.9992119,0.0003624084,0.00004046053,0.0001873402,0.0001232241,0.00007463594],"domain_scores_gemma":[0.9977751,0.001664871,0.0002071197,0.00006947201,0.0002556419,0.00002784904],"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":[0.00001965349,0.00004476008,0.001916868,0.00003274567,0.00003255533,0.00001753397,0.00001104117,0.9862854,0.0004129352,0.000388983,0.0002731057,0.01056447],"study_design_scores_gemma":[0.000001356471,0.000009971555,0.0003344589,0.000002409198,0.000004265601,0.000002081217,0.000006102923,0.9991934,0.0001430315,0.0002330452,0.00006781647,0.000002155108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3483729,0.001454062,0.6449574,0.0005122648,0.00008311235,0.0001022783,0.0005630347,0.001111789,0.002843268],"genre_scores_gemma":[0.9221137,0.0006790688,0.07415017,0.00007918819,0.00003540259,0.0001598907,0.0008866615,0.0001032842,0.00179261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01747347,"threshold_uncertainty_score":0.03474349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03436818284546374,"score_gpt":0.3856380421691437,"score_spread":0.3512698593236799,"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."}}