Calculation of uncertainty in calibration of microphones by the pressure reciprocity technique
Bibliographic record
Abstract
At the primary level, acoustical measurement standards are realized through calibration of the sensitivity level of Laboratory Standard microphones by the reciprocity technique. The technique is described in International Standard IEC 61094-2, which allows for various implementations of the measurement method. The pressure sensitivity levels of a set of three microphones are determined from the electrical and acoustical transfer impedances of pairs of the microphones. The transfer impedances in turn depend on the design and performance of the measurement apparatus, the dimensions and acoustical properties of the microphones and the cavity that acts as an acoustical coupler between the microphones, and the prevailing environmental conditions. The uncertainty in the pressure sensitivity level depends on the uncertainties in these input quantities and on how the sensitivity level varies with changes in the input quantities. The ISO/IEC Guide Express: 1995 Guide to the Expression of Uncertainties in Measurement provides internationally agreed models and guidance for evaluating the expanded uncertainty of a measurement. The uncertainty model, the nature of the input variables, and the steps involved in the calculation of the expanded uncertainty are described for the realization of a particular implementation of the reciprocity technique.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".