A standardised framework for the design and application of fine-scale acoustic tracking studies in aquatic environments
Bibliographic record
Abstract
Fine-scale tracking technology has advanced our understanding of aquatic animal behaviour by deriving near-continuous movements of animals ranging in size from small invertebrates to large predatory fish. Commercial fine-scale positioning systems, such as the VEMCO Positioning System, can pinpoint an animal’s location within metres of its true position. While methodological applications of commonly used presence-absence acoustic telemetry have identified factors that can limit array performance, the factors that influence position yield and accuracy and introduce error in fine-scale positioning systems have yet to be synthesised. Evidenced through a systematic review of the literature, we highlight key considerations and potential pitfalls faced when designing and conducting a fine-scale tracking study. Key factors impacting data acquisition are grouped under 4 key categories linked to the study system, species studied, and logistical and technological constraints. Thereafter, in line with these categories, we provide a framework that can be used prior to, during, and post-study to identify sources of error and data loss to optimize system design and acquired results. We provide details on user assessment tools that include a pre-study trial period using fixed tags to assess array geometry and data yield, an in situ checkpoint data download, and a post-study assessment of fixed transmitter performance. We highlight the utility of this framework and integrated assessment tools by presenting a real-world case study that ultimately was compromised. We anticipate that this framework can be used to standardize reporting of essential steps and checks that will generate comparable data for future synthesis, which will further advance fine-scale tracking approaches.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".