{"id":"W4366765123","doi":"10.1016/j.autcon.2023.104885","title":"Deep learning-based active noise control on construction sites","year":2023,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Noise Effects and Management","field":"Health Professions","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Canada Foundation for Innovation; Research Manitoba","keywords":"Active noise control; Feed forward; Noise (video); Noise control; Controller (irrigation); Transient (computer programming); Engineering; Noise pollution; Broadband; Computer science; Attenuation; Nonlinear system; Electronic engineering; Control (management); Control engineering; Control theory (sociology); Telecommunications; Channel (broadcasting); Artificial intelligence; Noise reduction","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":[0.0004657819,0.0007314595,0.000780353,0.0004926193,0.0003633903,0.0006389831,0.00125233,0.001000065,0.001991704],"category_scores_gemma":[0.00120834,0.0004183751,0.0006731555,0.0005321743,0.000447108,0.0007115435,0.0009741947,0.001105203,0.0004165592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007186802,"about_ca_system_score_gemma":0.0009588201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01777354,"about_ca_topic_score_gemma":0.02110523,"domain_scores_codex":[0.9996853,0.00004695378,0.000009880247,0.00009335541,0.00006792437,0.00009656513],"domain_scores_gemma":[0.9995384,0.0001743171,0.00003492614,0.00004537257,0.0001626924,0.00004432938],"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":[0.0003199302,0.0002409314,0.002708931,0.00006197512,0.00005508896,0.00006624231,0.00004500529,0.8220344,0.006808589,0.001662894,0.002318373,0.1636776],"study_design_scores_gemma":[0.000003083656,0.00001222568,0.00025953,0.000002162379,0.000003497874,0.000002165867,0.000005031086,0.9986223,0.0005803109,0.0004092389,0.00009840339,0.000002003097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2463465,0.0006468539,0.7451751,0.0004349977,0.0001871059,0.00004287909,0.0003261967,0.001980232,0.004860302],"genre_scores_gemma":[0.9645756,0.0001033827,0.03055082,0.00008860081,0.00003544274,0.0000285474,0.0003889369,0.00006733077,0.004161456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01777354,"threshold_uncertainty_score":0.03534019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02029694817886248,"score_gpt":0.3481074479020969,"score_spread":0.3278104997232344,"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."}}