The influence of environmental parameters on the performance and detection range of acoustic receivers
Bibliographic record
Abstract
Summary Acoustic telemetry is being increasingly used to study the ecology of many aquatic organisms. This widespread use has been advanced by national and international tracking programs that coordinate deployment of passive acoustic telemetry networks on a regional and continental scale to detect tagged animals. While it is well‐known that environmental conditions can affect the performance of acoustic receivers, these effects are rarely quantified despite the profound implications for tag detection and hence the ecological inferences. Here, we deployed eight receivers at different depths within the water column and at different orientations (hydrophone up or down) and 12 tags 200–800 m from the receivers for 234 days to investigate how the tag detection range of acoustic receivers varied through time and under different meteorologic and oceanographic conditions. The study showed that receiver depth and orientation, and time since deployment had the largest effect on the detection range. Thermocline gradient and depth, and wind speed were the environmental factors most affecting detection range, while wind direction, precipitation and atmospheric pressure had negligible or no effect. Comparison of results to a proposed general acoustic theory model and previous studies showed that findings from specific habitat types cannot be generalised and applied across other habitats or environments. A good understanding of the acoustic coverage and temporal variations in relation to environmental conditions are crucial to accurate interpretation of results, and ensuing management recommendations. We recommend that each study include stationary reference tags to measure changes in detection probability with time, help refine detection range, and be used to improve confidence in the reporting and interpretation of the data.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".