Networked Animal Telemetry in the Northwest Atlantic and Caribbean Waters
Bibliographic record
Abstract
Abstract Acoustic telemetry, in which transmitters projecting ultrasonic signals carrying unique identification codes are deployed on marine and aquatic animals and detected and logged by acoustic receivers, is becoming a common tool in fisheries science. Collaboration among researchers using this technology has led to the development of telemetry networks that are capable of detecting transmitters at coastwide and even continental scales through the combined coverage of all members’ receivers. Two grassroots telemetry networks in the northwest Atlantic and Caribbean, the Atlantic Cooperative Telemetry (ACT) Network and the FACT Network, began as small-scale efforts among neighboring researchers and have expanded to include shared databases of tagged animals along entire coastlines. A third telemetry network, the Ocean Tracking Network (OTN), has brought additional capacity to the ACT and FACT networks and has provided a focus for telemetry activities in Canadian waters. It has also improved the power and efficiency of telemetry research globally through collaborative, standardized methods for storing, sharing, and processing data. When used in combination with other data collected by traditional fishery research methods and emerging technologies, such as remote sensing and autonomous vehicles, data collected through acoustic telemetry networks can address fundamental but previously unanswered questions about key habitat areas and data-poor species and can yield new insights into the ecology of species that are thought to be well known. Here, we provide an overview of acoustic telemetry networks, including a history of the ACT Network, FACT Network, and OTN and a review of recent and current research that has been made possible through the connections enabled by these networks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".