{"id":"W4320734343","doi":"10.3397/in_2022_0966","title":"An example of a digital engagement platform for large scale community engagement using auralization.","year":2023,"lang":"en","type":"article","venue":"NOISE-CON proceedings","topic":"Noise Effects and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Train; Community engagement; Context (archaeology); Computer science; Key (lock); Customer engagement; Scale (ratio); Social media; Transport engineering; Engineering; World Wide Web; Public relations; Computer security; Political science; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002258116,0.0007251267,0.0003144545,0.001530277,0.001419285,0.003426882,0.001207618,0.001731418,0.0388107],"category_scores_gemma":[0.004505333,0.0002457527,0.0006822324,0.001156679,0.0007727289,0.003221192,0.006482853,0.001362754,0.01101747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000423342,"about_ca_system_score_gemma":0.0008074855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001271245,"about_ca_topic_score_gemma":0.002535395,"domain_scores_codex":[0.9982749,0.0007778943,0.00007070259,0.0001952936,0.0004484367,0.0002327469],"domain_scores_gemma":[0.9961938,0.001624689,0.0001355041,0.0008229075,0.0003710185,0.0008521639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001323228,0.001659242,0.008628255,0.002101345,0.0001317872,0.003597685,0.0208779,0.002216225,0.03832622,0.03198706,0.1533121,0.7358391],"study_design_scores_gemma":[0.0001422549,0.0007619712,0.009639667,0.0004962626,0.00006749271,0.001558948,0.006600638,0.01122243,0.009224066,0.01538752,0.9447246,0.0001741572],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1049742,0.001295493,0.4342366,0.00568999,0.001711597,0.003880216,0.005318282,0.04513892,0.3977548],"genre_scores_gemma":[0.45351,0.0009754553,0.3613217,0.002160037,0.00042458,0.003410954,0.005277667,0.003167707,0.1697519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0388107,"threshold_uncertainty_score":0.1298347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2289332183494836,"score_gpt":0.4156396203746949,"score_spread":0.1867064020252113,"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."}}