{"id":"W2264820321","doi":"10.1108/lht-04-2015-0034","title":"Reducing noise in the academic library: the effectiveness of installing noise meters","year":2016,"lang":"en","type":"article","venue":"Library Hi Tech","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Decibel; Noise (video); QUIET; Computer science; Intervention (counseling); Noise control; Noise measurement; Noise reduction; Psychology; Telecommunications; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.002668796,0.0001357733,0.000200392,0.0003657901,0.0001073835,0.0001853556,0.001964332,0.0000819105,0.0002889425],"category_scores_gemma":[0.0002784566,0.0000551294,0.0001150456,0.001372713,0.000136498,0.005466835,0.000363207,0.0002297326,0.00009619266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008864598,"about_ca_system_score_gemma":0.0000469727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000636595,"about_ca_topic_score_gemma":3.20002e-7,"domain_scores_codex":[0.9976122,0.0006014918,0.0005711964,0.0002473829,0.0007488437,0.0002189227],"domain_scores_gemma":[0.996065,0.003079887,0.0002233913,0.0005767733,0.00001547299,0.00003944055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001024673,0.0001872376,0.6101738,0.0001199605,0.00005627311,0.00003355,0.007221908,0.0003336936,0.05534161,0.06551635,0.03998837,0.2200025],"study_design_scores_gemma":[0.00133684,0.0001249898,0.8309736,0.0006311652,0.00003713844,0.000007938704,0.003468685,0.0006786351,0.05856013,0.04184561,0.06190226,0.0004330439],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831955,0.0001276079,0.0004063335,0.005227135,0.000146277,0.0004344325,0.00001944822,0.00007855768,0.01036469],"genre_scores_gemma":[0.9976633,0.00008058917,0.0002901105,0.0006961941,0.00004518832,0.00004585304,0.000005007139,0.0000131148,0.001160675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2207997,"threshold_uncertainty_score":0.3963323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217087458083611,"score_gpt":0.3678362187543351,"score_spread":0.246127472945974,"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."}}