{"id":"W4389228202","doi":"10.3397/in_2023_0495","title":"A precision sound pressure level measurement system","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":"Traceability; Noise (video); System of measurement; Measurement uncertainty; Metrology; Sound pressure; Computer science; Accuracy and precision; Noise measurement; Digitization; Acoustics; Systems engineering; Engineering; Telecommunications; Noise reduction; Artificial intelligence; Statistics; Physics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000613705,0.0002537291,0.0002579565,0.0002235178,0.0001334803,0.00009482982,0.0003122505,0.0001928781,0.00001112876],"category_scores_gemma":[0.0001018988,0.0002422966,0.00006261218,0.0005018447,0.00002109503,0.0002545681,0.00007790279,0.0002695555,0.0001524073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003647828,"about_ca_system_score_gemma":0.00002168005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002581335,"about_ca_topic_score_gemma":0.000001426377,"domain_scores_codex":[0.9981763,0.000005902489,0.0003632957,0.0003212375,0.0006308246,0.000502397],"domain_scores_gemma":[0.9992886,0.00002813172,0.00006222896,0.0001713145,0.0003007306,0.0001489481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003439973,0.00006638927,0.03758629,0.03656484,0.0006936848,0.0000417269,0.01233184,0.002285447,0.2589652,0.01770411,0.4550735,0.178343],"study_design_scores_gemma":[0.002549716,0.0005378068,0.4575446,0.005498131,0.0003332646,0.0001781906,0.002454498,0.09873844,0.2838543,0.01093721,0.1342614,0.003112431],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9803595,0.0004300239,0.0003204174,0.00008636312,0.001709431,0.0009037186,0.00002013353,0.01029688,0.005873564],"genre_scores_gemma":[0.9975861,0.0000340205,0.001394276,0.0000123678,0.0004277145,0.0002245401,0.00000274323,0.00008498615,0.000233279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4199583,"threshold_uncertainty_score":0.9880571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07243776667332384,"score_gpt":0.2849072664128468,"score_spread":0.2124694997395229,"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."}}