{"id":"W1600511398","doi":"","title":"Nose conedevice and built-in calibration check as essenti al features of a standalone instrument for unattended mid- And long-term noise measurements","year":2012,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Industrial and Mining Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microphone; Noise (video); Sound level meter; Acoustics; Calibration; Metre; Ambient noise level; Environmental noise; Engineering; Term (time); Noise measurement; Background noise; Computer science; Noise level; Sound (geography); Sound pressure; Physics; Noise reduction; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001409713,0.0001127914,0.0001484993,0.00009915697,0.0000391259,0.00001703104,0.00004529542,0.0001081934,0.00000928066],"category_scores_gemma":[0.00005726601,0.0001202876,0.00001380866,0.00006926904,0.00003411122,0.00009766507,0.000009768854,0.00008874613,3.479592e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419316,"about_ca_system_score_gemma":0.0001231033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002790032,"about_ca_topic_score_gemma":0.009307116,"domain_scores_codex":[0.9993571,0.00001006847,0.000164214,0.00009239166,0.00009649788,0.0002796993],"domain_scores_gemma":[0.999572,0.00002212138,0.00002977862,0.00008472284,0.00004055598,0.0002508039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003675129,0.0001916695,0.3654735,0.001947107,0.0005460865,0.00006644757,0.006466895,0.001907525,0.5857207,0.001936612,0.01001383,0.02536219],"study_design_scores_gemma":[0.003958977,0.0001424516,0.9480078,0.0003004065,0.0002115273,0.00002832668,0.0005092515,0.003541566,0.04206985,0.00004989776,0.0006183861,0.0005615885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974445,0.0004443423,0.0007910999,0.00009772962,0.0003963101,0.0003268359,0.0001713718,0.00001560668,0.0003122739],"genre_scores_gemma":[0.999115,0.00003624516,0.0005778578,0.00009693924,0.00006688553,0.00001078356,0.00002807914,0.00001881767,0.00004942674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5825343,"threshold_uncertainty_score":0.5193589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03341047461790397,"score_gpt":0.2551406914861563,"score_spread":0.2217302168682523,"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."}}