Advances in Fish Tagging and Marking Technology
Bibliographic record
Abstract
Abstract.—We present a new acoustic telemetry method for efficiently estimating positions of tagged marine and freshwater animals using vessel-based active tracking. Vessel-based tracking can require considerable time and effort, which limits both study area size and the number of tagged animals possible for telemetry studies. However, the recently-developed SYNAPS (Synthetic Aperture Positioning System) tracking method allows efficient collection of fine-scale movement information for many tagged animals within large study areas, and therefore enables large surveys to be conducted in a methodical and cost-effective manner. SYNAPS computes position estimates of tagged animals by means of hyperbolic positioning using the geographic location of a mobile hydrophone synchronized with signal detections to create a synthetic hydrophone array. Here we describe the process of tracking with SYNAPS, quantify accuracy and precision of position estimations, and provide guidelines for tracking procedures. SYNAPS position estimates were compared with known positions of fixed tags in both marine (Alaska, USA) and freshwater lake (Ontario, Canada) systems. Accuracy of position estimates ranged from 1.2 m using hull-mounted hydrophones and survey-quality GPS equipment to 23.4 m using towed hydrophones and a navigation-grade GPS receiver. This new tool will facilitate spatially explicit management applications such as aquatic protected area design and essential fish habitat designation by increasing the ability of acoustic telemetry to characterize movement of marine animals at different scales.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".