Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections
Bibliographic record
Abstract
Abstract Telemetry, or the remote monitoring of animals with electronic transmitters and receivers, has vastly enhanced our ability to study aquatic animals. Radio telemetry, acoustic telemetry and passive integrated transponders are three common technologies that generate detection data — time‐stamped, tag‐specific records that are logged by receivers. We review current statistical methods and comment on potential future directions for analysing detection data derived from fixed telemetry receiver arrays. To illustrate how different methods may be used to achieve diverse study objectives, we provide a case study dataset collected by an array of 42 acoustic telemetry receivers on 187 bull trout in the Kinbasket Reservoir of British Columbia. To close, we present a decision tree for guiding the selection of a method based on study objectives and sampling design. This paper provides both experienced and novice telemetry researchers with the knowledge and tools to facilitate more comprehensive analysis of detection data and, in so doing, ask a wide variety of ecological questions that will enhance our understanding of aquatic organisms.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".