{"id":"W4389240832","doi":"10.3397/in_2023_0494","title":"On the test method for short-term level fluctuation of sound calibrators","year":2023,"lang":"en","type":"article","venue":"NOISE-CON proceedings","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sound level meter; Noise (video); Distortion (music); Sound pressure; Sound (geography); Term (time); Acoustics; Engineering; Accuracy and precision; Metre; Test (biology); Computer science; Noise level; Electronic engineering; Physics; Mathematics; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004001973,0.0008838073,0.0007294223,0.0017997,0.0007847557,0.001547549,0.002867277,0.001836887,0.004109539],"category_scores_gemma":[0.01317347,0.0005967896,0.0009128537,0.001364266,0.002087796,0.002422278,0.001281664,0.002355975,0.002555787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316162,"about_ca_system_score_gemma":0.001088592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583199,"about_ca_topic_score_gemma":0.0009986716,"domain_scores_codex":[0.9898985,0.002082334,0.000287326,0.001327744,0.00614425,0.0002598152],"domain_scores_gemma":[0.9906695,0.005397995,0.0005196627,0.001238968,0.002080882,0.00009304613],"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":[0.0005309913,0.0002403993,0.004727107,0.0008897576,0.00009878764,0.0006986702,0.0009892628,0.0329308,0.1933922,0.2974854,0.00597465,0.462042],"study_design_scores_gemma":[0.00009110541,0.0009456254,0.004177626,0.000296397,0.0001035889,0.003427729,0.0002316352,0.612313,0.2617667,0.06927072,0.04708951,0.0002863959],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003044745,0.0003151302,0.9927255,0.0001145758,0.00008120977,0.00006814089,0.0000245482,0.0005726977,0.003053512],"genre_scores_gemma":[0.3299958,0.0008678581,0.6587443,0.0005148283,0.0002254905,0.0005499509,0.0003303265,0.0005635803,0.008207928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004109539,"threshold_uncertainty_score":0.02116472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06757389107007425,"score_gpt":0.3352448712105108,"score_spread":0.2676709801404366,"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."}}