Short-term level fluctuation of sound calibrators : measurement methods and their uncertainties
Bibliographic record
Abstract
A sound calibrator contains a stable sound source that can be coupled to the microphone of a measuring instrument. This allows the calibration of an entire measurement system prior to a measurement. The calibration of the measurement system is so important that many noise regulations specify the requirement of calibration. The sound calibrator itself needs to be calibrated routinely traceable to national standards. The International Standard IEC 60942:2003, Electroacoustics - Sound calibrators, specifies performance requirements for the sound pressure level generated by a sound calibrator, especially the short-term level fluctuation of sound pressure. The aim of this paper is to evaluate several methods proposed for the measurement of fluctuation in the sound pressure level generated by the sound calibrator. Specifically, this paper focuses on the sound level meter method, the analyzer method (using either a FFT analyzer or a set of 1/n octave band filters), and the time capture method. The analysis is emphasized on the key points for implementing IEC 60942:2003. The time capture method implemented at National Research Council Canada has a measurement uncertainty of 0.016 dB, which is better than the maximum permitted expanded uncertainty of measurement for short-term level fluctuation specified in IEC 60942:2003.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".