Evaluation of acoustic telemetry grids for determining aquatic animal movement and survival
Bibliographic record
Abstract
Abstract Acoustic telemetry studies have frequently prioritized linear configurations of hydrophone receivers, such as perpendicular from shorelines or across rivers, to detect the presence of tagged aquatic animals. This approach introduces unknown bias when receivers are stationed for convenience at geographic bottlenecks (e.g. at the mouth of an embayment or between islands) as opposed to deployments following a statistical sampling design. We evaluated two‐dimensional acoustic receiver arrays (grids: receivers spread uniformly across space) as an alternative approach to provide estimates of survival, movement and habitat use. Performance of variably spaced receiver grids (5–25 km spacing) was evaluated by simulating (1) animal tracks as correlated random walks (speed: 0.1–0.9 m/s; turning angle SD : 5–30°); (2) variable tag transmission intervals along each track (nominal delay: 15–300 s); and (3) probability of detection of each transmission based on logistic detection range curves (mid‐point: 200–1,500 m). From simulations, we quantified (i) time between successive detections on any receiver (detection time), (ii) time between successive detections on different receivers (transit time), and (iii) distance between successive detections on different receivers (transit distance). In the most restrictive detection range scenario (200 m), the 95th percentile of transit time was 3.2 days at 5 km, 5.7 days at 7 km and 15.2 days at 25 km grid spacing; for the 1,500 m detection range scenario, it was 0.1 days at 5 km, 0.5 days at 7 km and 10.8 days at 25 km. These values represented upper bounds on the expected maximum time that an animal could go undetected. Comparison of the simulations with pilot studies on three fishes (walleye Sander vitreus , common carp Cyprinus carpio and channel catfish Ictalurus punctatus ) from two independent large lake ecosystems (lakes Erie and Winnipeg) revealed shorter detection and transit times than what simulations predicted. By spreading effort uniformly across space, grids can improve understanding of fish migration over the commonly employed receiver line approach, but at increased time cost for maintaining grids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| 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 | 0.000 |
| 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".