Discussion on uncertainties for potential new sound-in-air primary standard based on the optical measurement of sound pressures in free-field conditions
Bibliographic record
Abstract
Laboratory and working standard microphones are calibrated using the current international standard based on the reciprocity method yielding the pressure and free-field sensitivities of the device under test. The typical uncertainties associated with reciprocity are so low that they exceed the requirements of the most demanding end-user applications, and so the drive for improvement from an uncertainty perspective is low. However, reciprocity also limits the microphone technologies that can be calibrated as it is only applicable to condenser microphones of specific characteristics, standards and dimensions. Microphones, in the broader sense, vary with regards to dimensional and even technological perspectives and in addition are used in free-field conditions. As such, a potential new primary standard that can accommodate and calibrate any microphone for airborne and audible frequencies has been developed based on the photon correlation method in free-field conditions. In this case, the pressure of a propagating sound field in a fully anechoic chamber is measured remotely for a range of specific frequencies, either set or even user-defined. Following, the device under test is placed at the same point of the propagating sound and its electrical output is combined with the optically measured pressure to yield the device sensitivity directly and in an absolute manner. This paper discusses the properties and numerical contributions of potential uncertainty factors associated with the proposed new standard.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".