Close proximity detection interference with acoustic telemetry: the importance of considering tag power output in low ambient noise environments
Bibliographic record
Abstract
When employing acoustic telemetry to study aquatic species, understanding the functional dynamics of the monitoring system is essential for effective study design, data interpretation, and analysis. Typically, researchers are concerned with maximum effective detection range and consequently tend to employ the largest most powerful tags the study species can carry without considerable energetic burden. In ideal acoustic conditions of low ambient noise environments, low attenuation, and reflective structure, higher powered tags can be detected at larger distances from the receiver, but they can also be subject to the phenomenon ‘Close Proximity Detection Interference’ (CPDI). This occurs when reflective barriers, such as a calm water surface and/or hard substrate, result in strong transmission echoes that interfere with the transmission sequence. As a result, transmissions in close proximity to the receiver are not effectively decoded and logged. CPDI was assessed from the results of three detection range tests conducted using the Vemco 69 kHz telemetry system in three contrasting study systems: a sheltered marine Arctic embayment, a temperate freshwater lake, and an exposed marine sub-tropical reef line. For the Arctic embayment, CPDI was absent with the lower power V9 tag (90% of transmissions received at 55 m) but was recorded for the V13 tag and was most prevalent for the highest power V16 tag (18% and 8% of transmissions received at 55 m, respectively). Comparing V16 tag detection profiles between study systems, CPDI was evident in the low ambient noise Arctic embayment and temperate freshwater lake (highest transmission proportions recorded at 370 and 207 m, respectively) but was absent on the high ambient noise sub-tropical reef line. Functional examples highlight the ways in which CPDI can affect different study designs if not acknowledged or accounted for. CPDI was shown to be the most prominent in low ambient noise study systems and should be considered when choosing tag type/power during study design. If unaccounted for, CPDI could lead to misinterpretation during the analysis of acoustic telemetry data. The identification of CPDI highlights the complexities associated with the functionality of acoustic telemetry systems and supports recommendations for thorough detection range testing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".