Validation of primary hydrophone calibrations by inter-laboratory comparisons and by independent calibration methods
Bibliographic record
Abstract
A description is presented of two approaches which may be used to validate primary calibration methods for hydrophones and transducers. Firstly, a comparison may be made with another independent absolute calibration method, preferably one based on a different physical principle (and therefore with few common sources of uncertainty). Secondly, an inter-laboratory comparison of calibrations may be undertaken between different institutes operating at a similar level. This paper describes the results of such exercises for free-field calibration of hydrophones in the range from 1 kHz to 500 kHz. Firstly, two independent calibration methods are compared: the three-transducer reciprocity method and a method based on optical interferometry. The differences observed in the results are typically less than 0.5 dB, which is of the same order as the overall uncertainties of each of the methods. Secondly, the results are shown of a recent international comparison of hydrophone calibrations involving institutes from Canada, China, Germany, Russia, South Africa, UK, and USA. Here, the agreement was generally within quoted uncertainties, the results generally lying within a ±0.5 dB band for frequencies up to 300 kHz. A discussion is given of the general sources of uncertainties in the calibrations.
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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 | 0.033 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".