Integrating acoustic telemetry research into management: successes and challenges in the Laurentian Great Lakes
Bibliographic record
Abstract
In the Laurentian Great Lakes, the application of acoustic telemetry to track fish movements has evolved into an important part of multijurisdictional management. Nevertheless, barriers remain in translating telemetry research into management or conservation actions. Here, we synthesize acoustic telemetry literature within the Great Lakes basin to explore factors that have contributed to successes and failures of integrating research with the needs of decision-making processes. Collaboration between researchers and managers, facilitated by consistent opportunities for stakeholder engagement, stood out as one of the most effective means of integration. For example, 79% (95 of 127) of articles published (up to 2023) included co-authorship by both government and academic organizations. Case studies on lake sturgeon ( Acipenser fulvescens), walleye ( Sander vitreus), and sea lamprey ( Petromyzon marinus) further highlight how telemetry has informed management through collaborative engagement among researchers, stakeholders, and managers, as well as ongoing challenges. By exploring facets of acoustic telemetry research and connections to conservation and fisheries concerns, we identify pathways to reduce knowledge–action gaps widely applicable within and outside of the Great Lakes.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".