{"id":"W2763395168","doi":"10.47339/ephj.2017.87","title":"The practicality of using a smartphone as a sound level meter","year":2017,"lang":"en","type":"article","venue":"BCIT Environmental Public Health Journal","topic":"Noise Effects and Management","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariate analysis of variance; Sound level meter; Android (operating system); Phone; Noise (video); Sound (geography); Smartphone application; Engineering; Computer science; Acoustics; Telecommunications; Multimedia; Noise level; Sound pressure; Operating system; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.01017188,0.001695401,0.0007360033,0.001778719,0.001586214,0.003798394,0.002947879,0.003914208,0.02599326],"category_scores_gemma":[0.05947441,0.0009186552,0.001000735,0.0008680699,0.001867084,0.005747794,0.002874509,0.002732726,0.01637743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009181757,"about_ca_system_score_gemma":0.001533797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00278738,"about_ca_topic_score_gemma":0.004100284,"domain_scores_codex":[0.9858347,0.005373206,0.0009415285,0.001619449,0.005754274,0.0004768285],"domain_scores_gemma":[0.9423189,0.03065849,0.003289497,0.007681522,0.01456507,0.001486478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001558159,0.001432282,0.08171643,0.002770764,0.0002372071,0.005421944,0.006025575,0.002024473,0.04242016,0.006368148,0.07372541,0.7762994],"study_design_scores_gemma":[0.0006186921,0.01082474,0.140485,0.005258958,0.001056881,0.0550757,0.02005303,0.02021815,0.04949341,0.02316746,0.6726831,0.001064898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3218464,0.01440786,0.3333292,0.1066341,0.009498626,0.005010372,0.002459357,0.01394118,0.1928729],"genre_scores_gemma":[0.5573307,0.005337724,0.3810061,0.01490171,0.002783662,0.00195403,0.0008940332,0.0008142486,0.03497772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02599326,"threshold_uncertainty_score":0.08695608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3622000714304306,"score_gpt":0.5025679151624535,"score_spread":0.1403678437320228,"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."}}