Field trial of a Doppler sonar system for fisheries applications
Bibliographic record
Abstract
Various deployments of commercial Doppler current profiling systems have demonstrated that these instruments can detect fish and measure their swimming speeds. However, research into the possible application of Doppler sonar to fisheries problems is limited and has not taken advantage of coherent signal processing schemes. A field trial was undertaken in August 2002 to explore the capabilities of a coherent Doppler sonar when applied to detecting discrete targets. The passage of migrating salmon on the Fraser River in British Columbia provided an ideal test opportunity with fish of well-defined swimming behavior and allowed for comparisons with conventional fisheries acoustics techniques. The instrument tested was a 250-kHz sonar which provided for phase coding of transmit pulses and coherent sampling of successive acoustic returns. The field trial resulted in 11 consecutive days of Doppler sonar data acquired during the peak of the sockeye salmon (Oncorhynchus nerka) migration. A total of 7425 individual fish were identified and their swimming speed was measured with an accuracy of between 10 cm s−1 and 20 cm s−1, which depended on pulse length, pulse spacing, and target range. By comparison, water velocity measurements made with the same instrument can only achieve a theoretical accuracy of 60 cm s−1.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".