Evaluation of discrete target detection with an acoustic Doppler current profiler
Bibliographic record
Abstract
Abstract Acoustic Doppler current profilers (ADCPs) use the signal scattered back from small suspended drifting particles, such as suspended sediments and planktonic species, to measure water current velocity. Additionally, ADCPs detect signals scattered from fish and other pelagic organisms, but these signals are generally treated as noise and rejected during data processing. However, these rejected signals can contain information on fish movement, which presents an opportunity to extend the application of ADCP technology as a tool for monitoring fish activity. We compare discrete target counts made using an ADCP with those of a split‐beam echosounder in a region of high tidal currents in the Bay of Fundy. The comparison was achieved using a self‐contained bottom‐mounted frame equipped with both a 600‐kHz Teledyne RD Instruments Workhorse ADCP and a 120‐kHz BioSonics DTX Submersible split‐beam echosounder system. The discrete targets were identified using a combination of signal correlation and volume backscatter (SV) thresholds in the ADCP data and using a fish tracking algorithm in the split‐beam echosounder data. The resulting ADCP discrete target counts agreed with fish tracking counts made from the co‐located split‐beam echosounder. Notably, discrete targets recorded by the ADCP could be differentiated from entrained air using a high signal correlation threshold. This method can expand the application of ADCP data for biophysical assessments for fisheries management and could be of particular use in rivers and tidal channels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".