Effects of radio‐collar position and orientation on GPS radio‐collar performance, and the implications of PDOP in data screening
Bibliographic record
Abstract
Summary Global positioning system (GPS) radio‐telemetry has become an important wildlife research technique worldwide. However, understanding, quantifying and managing error and bias in raw GPS radio‐telemetry data sets requires much more work. In particular, error and bias resulting from position (angle away from vertical) and orientation (compass direction) of GPS radio‐collars on free‐ranging animals is currently unknown. We tested the effects of collar position and orientation on GPS radio‐collar performance using five stationary GPS radio‐collars. We also investigated the use of positional dilution of precision (PDOP) as a method for screening data with high location errors. Orientation had no statistical effect on fix rates or location errors. The biggest source of variation was attributed to collar position, which resulted in significantly lower performance at angles below 90° from vertical. PDOP‐based screening was effective and can be used to lower location error, but the trade‐off between higher location accuracy and data loss (potentially leading to new bias) must be assessed. Synthesis and applications. The results of this study refine our understanding of error and bias in GPS radio‐telemetry data. We suggest that collar orientation can safely be disregarded, whereas radio‐collar position remains a large potential source of error and bias. This finding has major implications regarding animal activity and GPS radio‐telemetry research. Researchers need to quantify and account for biases resulting from animals moving through heterogeneous terrain and habitats.
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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 | 0.028 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".