Strong Thermal Stratification Reduces Detection Efficiency and Range of Acoustic Telemetry in a Large Freshwater Lake
Bibliographic record
Abstract
Abstract BackgroundThe successful use of acoustic telemetry to detect fish hinges on understanding the factors that control the acoustic range. The speed-of-sound in water is primarily a function of density, and in freshwater lakes density is primarily driven by temperature. The seasonal thermal stratification in the Great Lakes represent the strongest sound speed gradients in any aquatic system. Such speed-of-sound gradients can refract sound waves leading to greater divergence of acoustic signal, and hence more rapid attenuation. The changes in sound attenuation change the detection range of a telemetry array and hence influence the ability to monitor fish. We use three months of data from a sentinel array of V9 and V16 Vemco acoustic fish tags, and a record of temperature profiles to determine how changes in stratification influence acoustic range in eastern Lake Ontario. ResultWe interpret data from an acoustic telemetry array in Lake Ontario to show that changes in acoustic detection efficiency and range correlate strongly with changes in sound speed gradients due to thermal stratification. The strongest sound speed gradients of 10.38 ms-1/m crossing the thermocline occurred in late summer, which caused the sound speed difference between the top and bottom of the water column to be greater than 60 m/s. V9 tags transmitting across the thermocline could have their acoustic range reduced from >650 m to 350 m, while the more powerful V16 tags had their range reduced from >650 m to 450 m. In contrast we found that when the acoustic source and receiver were both transmitting below thermocline there was no change in range, even as the strength of sound speed gradient varied. ConclusionChanges in thermal stratification occur routinely in the Great Lakes, on timescales between months and days. The acoustic range can be reduced by as much as 50% compared to unstratified conditions when fish move across the thermocline. We recommend that researchers consider the influences of thermal stratification to acoustic telemetry when configuring receiver position.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".