{"id":"W1992187358","doi":"10.1121/1.3277220","title":"Speech levels in meeting rooms and the probability of speech privacy problems","year":2010,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Public Works and Government Services Canada","keywords":"Computer science; Noise (video); Speech recognition; Ambient noise level; Acoustics; Sound (geography); Artificial intelligence; Physics","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":[0.002365405,0.000368899,0.0005601488,0.0009802053,0.0003564327,0.001016053,0.0003572873,0.0006816302,0.001866093],"category_scores_gemma":[0.02705189,0.0004333986,0.00043708,0.0004362125,0.0008938641,0.0009950924,0.0007676014,0.0006982952,0.0003572259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002323104,"about_ca_system_score_gemma":0.0002350742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005277573,"about_ca_topic_score_gemma":0.000441762,"domain_scores_codex":[0.9968726,0.0008476187,0.000415251,0.0006424465,0.0009900664,0.0002321629],"domain_scores_gemma":[0.9553343,0.03115891,0.007639362,0.001783587,0.002249374,0.00183449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002628842,0.0002784581,0.907783,0.0001807655,0.0003207345,0.0006792825,0.001454404,0.00601344,0.04485556,0.0001929909,0.0001820286,0.03543056],"study_design_scores_gemma":[0.00001158055,0.001364223,0.9785655,0.00001169581,0.0001053695,0.001604804,0.0006265467,0.005777449,0.01145481,0.0002310963,0.0001990532,0.00004788113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967648,0.0000950988,0.002635399,0.00002005261,0.000003958506,0.00001039203,0.00009331742,0.00003322214,0.0003439318],"genre_scores_gemma":[0.9987061,0.0000368936,0.001025857,0.000005520379,0.000008274901,0.000008855475,0.00008198577,0.00000931569,0.000117115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002365405,"threshold_uncertainty_score":0.01250964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848000784837933,"score_gpt":0.2787546023763965,"score_spread":0.2502745945280171,"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."}}