Multibeam sonars: Applications for fisheries research
Bibliographic record
Abstract
Multibeam sonars are rapidly becoming a standard tool for seafloor mapping in support of geological, geophysical, and engineering applications. More recently, the ability of multibeam sonars to provide high-resolution, large areal coverage, and potentially quantitative, coregistered, backscatter has been applied very successfully to problems of defining fisheries habitat. While most multibeam sonars are designed to gate out all midwater returns, newly developed systems now allow access to the full data stream and thus offer the possibility of application to studies of pelagic and demersal fisheries. Traditional acoustic approaches to fisheries issues have used single beam echo sounders that sample a relatively small volume of the water column within a survey area. Multibeam sonars provide a mechanism to greatly enhance both the resolution and the area of coverage. When combined with powerful new 3-D visualization techniques, they can offer immediate feedback on fish behavior as well as the critical question of vessel avoidance. If properly calibrated, multibeam sonars can provide the means for much more robust assessment of stock levels and perhaps even species identification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".