The old and the new: evaluating performance of acoustic telemetry systems in tracking migrating Atlantic salmon (<i>Salmo salar</i>) smolt and European eel (<i>Anguilla anguilla</i>) around hydropower facilities
Bibliographic record
Abstract
Acoustic telemetry represents the state-of-the-art technology for monitoring behaviour of aquatic organisms in the wild. Yet, the performance of different systems is rarely evaluated across species and environments. In this study, we evaluate two different acoustic telemetry systems, a commonly used analogue pulse-position-modulation-based system (VEMCO PPM) and a newly developed high-residency digital binary phase shift key-based system (VEMCO HR2), in ability to track downstream migrating Atlantic salmon smolt (Salmo salar) and European eel (Anguilla anguilla) around hydropower facilities. High-precision GPS were used to evaluate precision and accuracy of hyperbolically positioned data derived from each system. The PPM-based system had higher detection range than HR2 and generated more positions per transmission for eels migrating close to bottom than for surface-oriented salmon smolts. HR2 generated tenfold more positions per time unit than PPM, were less sensitive to noise, achieved submetre positional precision, and were considerably more accurate than PPM-derived positions after filtering. HR2 was deemed more capable than PPM in fine-scale positioning at moderate distances at hydropower facilities.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".