Modeling split-beam sonar to evaluate fish target strength accuracy
Bibliographic record
Abstract
Split-beam echosounders enable estimation of fish size by directly measuring their target strength. This measurement is achieved using compound transducer array geometries with accurate knowledge of resulting beam patterns. The multiple transducer components and required accurate calibration can present challenges in predicting the exact sonar capabilities. We present a model of the split-beam sonar system that can be used to evaluate and optimize sonar performance of a given transducer before committing to a hardware design. The model has been used to generate beam patterns to match prototype instruments and to simulate acoustic signals based on the scattering of sound from particles in a three-dimensional domain. Different split-beam sonar algorithms have been compared to measure the position and target strength of particles in the generated signal. The model's prediction capabilities were evaluated through comparisons with field trials of a prototype system. The field trials were conducted by lowering a calibration target sphere to a range of 200 m in the acoustic beam. Both model prediction and prototype system performance show accuracy of σ = ±0.2 dB at 25m range. Potential future applications of the model include exploring methods of target separation and improving accuracy when presented with complicated target structures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".