Hydrophone calibration between 1 kHz and 10 kHz using elastic waveguides
Bibliographic record
Abstract
Modern techniques for hydrophone calibration from IEC 60565:2020 typically require sensors to be placed in a free field or within a hydrostatically varying chamber. At mid-frequencies (defined as 1 kHz–10 kHz), the wavelength is too long for free-field conditions in tanks available to most manufacturers and academics. A novel technique to calibrate hydrophones is investigated to address the measurement gap between very-low-frequency (0.1 Hz–1 kHz) and high-frequency (10 kHz–200 kHz) techniques. The measurement environment consists of a 12-meter length of copper tubing that is coiled into a 1-meter diameter helix. Propagation in the elastic waveguide decreases the speed of sound within the apparatus and the wavelength relative to its free-space equivalent. This provides a longer reverb-free time within which to make the calibration measurements using a small (<1 m3) volume. To increase the gain of the system, the recorded files are matched filtered against normalized transmission replicas to determine the signal energy at the receiver. The propagation within the waveguide is studied, including investigating the modal dispersion and channel gain. Calibrations are performed on multiple Ocean Sonics icListen hydrophones, including independently calibrated reference units, using an uncalibrated source to determine the accuracy and precision of the system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".