Quantifying a Manual Triangulation Technique for Aquatic Ultrasonic Telemetry
Bibliographic record
Abstract
Abstract Ultrasonic telemetry is widely used to determine the large-scale migrations and movements of aquatic animals, but it can also be used to monitor fine-scale movements and habitat occupancy. We tested the accuracy and precision of a method that manually triangulates transmitter locations from a series of bearings recorded at known observer locations. Bearing estimates were based on the direction of the strongest transmitter pulse received at each stationary position. With a minimum of three bearing estimates, transmitter locations and 95% confidence ellipses were calculated. Testing within a blind experimental design showed that triangulation improved accuracy and precision when additional bearings were used and that it was optimized when bearings were recorded from widespread locations. Using 3–7 bearing positions, 95% of all location estimates were within 34 m of the actual transmitter. Triangulation is best suited for monitoring animals during periods of quiescence, and it can be used effectively to describe the extent of home ranges, spawning areas, and overwintering habitat, among other factors. Understanding the fine-scale habitat occupancy of aquatic animals is critical to species management, and triangulation can provide substantial improvements to the traditional location estimates used in ultrasonic telemetry. Received December 30, 2014; accepted June 2, 2015
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".