Rapid in situ range testing for acoustic telemetry in Leizhou Bay, South China Sea
Bibliographic record
Abstract
Acoustic telemetry is widely used to study the movement and habitat use of marine animals, but its effectiveness depends greatly on the appropriate receiver spacing and the local environmental context. In practice, when time or logistical resources are limited, short-term range tests, if carefully planned and interpreted, can provide rapid and valuable deployment guidance. Here, we conducted a 24-h in situ range test in a subtropical bay as a practical case study and reference workflow for planning receiver deployment before animal tracking studies. Two transmitters with different power outputs were deployed, and a generalized additive mixed model was applied to assess how environmental factors influenced detection proportion. The results showed that the effective detection range (50% detection proportion) was 170.4 m for the high-power transmitter and 114.3 m for the low-power transmitter. Mean detection proportion at nighttime was significantly higher than at daytime for both transmitter types. Distance to transmitter and water temperature significantly affected both transmitter types, while other factors, such as hour of day, wind, water depth, dissolved oxygen, background noise, and tilt angle, showed transmitter-specific effects. This study was not intended to develop universal predictive models from the 24-h dataset, but rather to determine appropriate receiver spacing and demonstrate how environmental variables can be incorporated to interpret rapid range test results under local conditions. The workflow provides a replicable and efficient approach for short-term range testing and data interpretation, offering practical guidance for optimizing acoustic telemetry deployment in similar environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".