Extraction and comparison of acoustic backscatter from a calibrated multi- and single-beam sonar
Bibliographic record
Abstract
Abstract Multi-beam sonar is potentially a powerful analytical tool for investigating the acoustic properties and behaviour of fish in relation to quantitative fisheries research. The SIMRAD SM2000 is a 200 kHz multi-beam sonar employing an 80-element array to transmit and synthesize, electronically, 128 receive beams (20°×2.2°) over a 180° arc simultaneously. Once calibrated, such systems enable the extraction of acoustic target strength and volume backscattering from an extended 3D ocean volume. We present an overview of the theoretical framework for the calibration of a multi-beam sonar, and then compare the acoustic backscatter from a calibrated single-beam 50 kHz echosounder with selected beams from a sphere-calibrated multi-beam sonar. Both systems recorded acoustic data from Atlantic herring contained within a weir, as the fish passed beneath the transducers. Specifically, we examine the relationship between the area-backscattering strength (Sa) from the single-beam system with the nadir beam (beam 63) of the SM2000 sonar. In addition, data are presented on the observed variability in Sa with target aspect for off-vertical angles from 15° to 60° in 15° intervals. Non-standard synthesized SM2000 beam widths are explored for both calibration and field datasets. The implications for biomass estimation are also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".