Extracting fish and water velocity from Doppler profiler data
Bibliographic record
Abstract
Abstract Zedel, L., and Cyr-Racine, F-Y. 2009. Extracting fish and water velocity from Doppler profiler data. – ICES Journal of Marine Science, 66: 1846–1852. Doppler current profilers are optimized for measuring water velocities, but have the demonstrated capability to measure fish swimming speeds. This is possible when fish form schools that are large enough for all multiple Doppler sonar beams to sample the fish speeds at the same time. In situations where fish are not present in at least three acoustic beams, it is impossible to extract fish velocity with the data-processing algorithms normally used to extract water velocity. We present an alternative method of analysing Doppler sonar data that treats data from individual acoustic beams independently, so that velocities can be extracted when fish appear intermittently in the sonar beams. The method determines the variance for each velocity estimate so that data averaging can be adjusted to achieve the desired accuracy. The algorithm is applied to extract both water and fish velocities from Doppler profiler observations of overwintering Atlantic cod (Gadus morhua) in Smith Sound, Newfoundland. Currents in this enclosed coastal area are slow (∼10 cm s−1), and the fish appear to move passively with the water much of the time. However, there are times when the fish have velocities different from those of the water, and profiles averaged over 20 d show clear differences in fish and water velocities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".