Monitoring fish movement using an ADCP
Bibliographic record
Abstract
An Acoustic Doppler Current Profiler, ADCP, can detect the presence of fish in water using the backscatter intensity. The Doppler profiler does not, however, make a point measurement; rather, measurement is made by averaging multiple beams and assuming that the current velocity is uniform between the distinct sample locations. As a result, individual fish speed cannot be measured. However, data are presented that demonstrate that the ADCP can measure the swimming speed of large fish schools. Fish speed and direction were measured for Norwegian spring herring. Observed speeds were 0–40 cm sec−1. Diel vertical migrations were observed with Norwegian herring ascending to the surface at dusk, and descending to greater depth at dawn. The accuracy and precision of an ADCP is a complex function of many variables (i.e., frequency, pulse length, transducer characteristics, backscatter strength, type and distribution of scatters). The other problems to be considered are sampling criteria and calibration. These sampling problems are explored for the case of ADCP measurements of fish movement. [Work supported by the Natural Sciences and Engineering Research Council of Canada and an Atlantic Canada Opportunities Agency Infrastructure grant.]
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".