Using the VEMCO Positioning System (VPS) to explore fine-scale movements of aquatic species: applications, analytical approaches and future directions
Bibliographic record
Abstract
Recent advancements in telemetry have redefined our ability to quantify the fine-scale movements of aquatic animals and derive a mechanistic understanding of movement behaviours. The VEMCO Positioning System (VPS) is a fine-scale commercial positioning system used to generate highly accurate semi-continuous animal tracks. To date, VPS has been used to study 86 species, spanning 25 taxonomic orders. It has provided fine-scale movement data for critical life stages, from tracking day-old turtle hatchlings on their first foray into the sea to adult fish returning to natal rivers to spawn. These high-resolution tracking data have improved our understanding of the movements of species across environmental gradients within rivers, estuaries and oceans, including species of conservation concern and commercial value. Existing VPS applications range from quantifying spatio-temporal aspects of animal space use and key aspects of ecology, such as rate of movement and resource use, to higher-order processes such as interactions among individuals and species. Analytical approaches have seen a move towards techniques that incorporate error frameworks such as autocorrelated kernel density estimators for home range calculations. VPS technology has the potential to bridge gaps in our fundamental understanding of fine-scale ecological and physiological processes for single and multi-species studies under natural conditions. Through a systematic review of the VPS literature, we focus on 4 principle topics: the diversity of species studied, current ecological and ecophysiological applications and data analysis techniques, and we highlight future frontiers of exploration.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".