A low-cost calibration facility for high-frequency acoustic backscatter instruments
Bibliographic record
Abstract
Full-system calibration of acoustic instruments used to measure high-frequency backscatter from zooplankton and small fish in the ocean is required if the data are to be used to estimate biomass and populations from signals at different frequencies. Here we describe a test tank facility for calibrating high-frequency, narrow-band sonars operating at one or more frequencies from 125 kHz to 775 kHz and with beam-widths between 1.8 and 11 degrees. The all steel tank is cylindrical with a diameter of 2.43 metres and a length of 6.10 metres and holds 28.5 cubic metres of water. The absorbing material on the end wall is a coarse artificial turf which is easily available at minimal cost and reduces high-frequency reverberations in the tank to negligible levels after 6 reflections. Pulsed operation at pinging rates up to 10 Hz is therefore possible without interference from reverberations. Echoes from the target are recorded over a series of pings; the difference between the target strength computed from the mean of those echoes and the known target strength is a measure of the accuracy of the nominal instrument response calculated from the manufacturer's transducer characteristics and bench measurements of the system electronics, and therefore allows a calibration correction to be made. An acoustic propagation model of the tank has also been developed to characterize the response of instruments placed in it and to identify optimum target placements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".