Use of CDMA Acoustic Telemetry to Document 3-D Positions of Fish: Relevance to the Design and Monitoring of Aquatic Protected Areas
Bibliographic record
Abstract
Knowledge of animal spatial ecology is essential for the design and siting of proposed aquatic protected areas (APAs), as well as the assessment and monitoring of existing ones. Acoustic telemetry is one of the primary tools for the assessment of animal movements in aquatic systems through either manual tracking or the establishment of fixed receiving stations. Recent technological developments in code division multiple access (CDMA) acoustic telemetry now enable the simultaneous real-time monitoring of numerous individual fish at fine time scales providing APA researchers with a robust new tool. Fish can be positioned in three-dimensions with sub-meter accuracy in both deep and shallow waters. Here, we describe a whole-lake environmental observatory that includes a 13-hydrophone acoustic telemetry array that has been used to monitor the position of 22 tagged fish at 15 sec intervals. Although we use a freshwater fish and environment as a case study, this telemetry system is equally useful for marine environments including under-ice. We evaluate the applicability of CDMA MAP technology to address pressing questions in applied APA research. The CDMA MAP system provides the flexibility to collect information at multiple spatial-temporal scales, responding to the varied levels of detail and precision required for different applications in APA research. When combined with the suite of other telemetry and monitoring approaches available, CDMA MAP technology will enable researchers to document the spatial ecology essential for improving APA science. Furthermore, because numerous animals from different trophic levels can be tracked in real time, CDMA MAP technology will also aid in our understanding of complex community-level dynamics consistent with the shift towards ecosystem-based APA management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".