Globally coordinated acoustic aquatic animal tracking reveals unexpected, ecologically important movements across oceans, lakes and rivers
Bibliographic record
Abstract
Acoustic telemetry is a popular approach used to track many different aquatic animal taxa in marine and freshwater systems. However, information derived from focal studies is typically resource‐ and geography‐limited by the extent and placement of acoustic receivers. Even so, animals tagged and tracked in one region or study may be detected unexpectedly at distant locations by other researchers using compatible equipment, who ideally share that information. Synergies through national and global acoustic tracking networks are facilitating significant discoveries and unexpected observations that yield novel insight into the movement ecology and habitat use of wild animals. Here, we present a selection of case studies that highlight unexpected tracking observations or absence of observations where we expected to find animals in aquatic systems around the globe. These examples span freshwater and marine systems across spatiotemporal scales ranging from adjacent watersheds to distant ocean regions. These unexpected movements showcase the power of collaborative telemetry networks and serendipitous observations. Unique and unexpected observations such as those presented here can capture the imagination of both researchers and members of the public, and improve understanding of movement and connectivity within aquatic ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".