Improving Fish Tracking Over Large Scale Oceans with Autonomous Marine Vehicles and a New Directional Acoustic Telemetry Receiver
Bibliographic record
Abstract
The use of autonomous marine vehicles equipped with onboard acoustic telemetry receivers has become increasingly popular for tracking aquatic animals over large scale oceans [1]. In this paper we present the results from at-sea trials of Innovasea's prototype advanced mobile receiver installed on both a Wave Glider and a Slocum glider and deployed in the Gulf of St. Lawrence. In at-sea range testing performed with both glider types, the redesigned receiver improved detection range by an average of 2.5 times that of a conventional receiver deployed in tandem as a baseline and made an average of 6.9 times more detections than the conventional receiver. Furthermore, during the at-sea mission deployment on the Wave Glider, the mobile receiver received 7.2 times more detections than the co-deployed conventional receiver. We demonstrated that the new mobile receiver enabled a significant increase in range relative to previous state-of-the-art acoustic telemetry technology, which ultimately resulted in an increased probability of detecting tagged animals at ocean-scale. The receiver should greatly increase the capability of autonomous vehicles carrying it to detect acoustically tagged animals in the vehicle's operation area.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".