Detection Range of Acoustic Receivers in a Large Hydropower Reservoir
Bibliographic record
Abstract
Acoustic telemetry manufacturers report estimated detection ranges under idealized conditions, but environmental conditions such as water depth, substrate type, and turbulence can affect the range of reliable detection. Range testing of low (Vemco V7 136 dB re 1µPa@1m) and high power (V13 147 dB re 1µPa@1m) acoustic transmitters (tags) was performed near a hydropower generating station and its associated reservoir using both active (mobile; VR100) and passive (stationary; VR2W/VR2Tx) receivers. Low power tags are typically used to track small fish such as juvenile Atlantic salmon (Salmo salar), whereas high power tags are typically used to track larger fish such as adult salmon. The results found herein were applied to concurrent salmon telemetry studies. Detection ranges of the low power tags were within 246–351 ± 20–70 m (mean ± SE), and the high power tags were within 537–1106 ± 53–272 m. Observed detection ranges were comparable or higher to manufacturer estimates for both tag types being detected by passive receivers, and were lower than expected for both tag types being detected by active receivers. Passive receivers were further tested by mooring a fixed sentinel tag (low power) on a receiver line at the hydropower site for 50 days. The sentinel tag detection range of 212 m was less than the expected range of 280–292 m, and was not found to be significantly impacted by wind speed. There was evidence of a hydropower effect on detection probability (up to 95% reduction) of both tag types for the active receiver, and detection ranges were significantly lower at the hydropower site than the reservoir site for the high power tag. The results of this study give insight to the initial design of acoustic telemetry studies beyond what can be gathered from manufacturer’s estimates, but rather near hydropower facilities and within large reservoirs; however, detection ranges reported herein do not replace the importance of range testing in site-specific conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".