Testing Global Positioning System Performance for Wildlife Monitoring Using Mobile Collars and Known Reference Points
Bibliographic record
Abstract
Abstract: To determine the spatial resolution of Global Positioning System (GPS) receiver data, rigorous testing is essential. We tested performance of the Lotek 3300 GPS collar for medium‐sized mammals (Lotek Engineering, Inc., Newmarket, ON, Canada). To mimic real wildlife monitoring situations, we performed both static (stationary receiver) and mobile tests, placing the receiver collar on a dog. We compared fix locations of the mobile receiver with the actual trajectory described by a portable Trimble high‐precision GPS. We determined performance in relation to habitat type and leaf cover. Location error was habitat‐dependent, with the best results in open habitat and much poorer ones in forest, particularly coniferous‐dominated forest. For both static and mobile tests, location accuracy was higher when the number of satellites contacted was high and when the residual positional dilution of precision (PDOP) value was low. However, location error was highly variable, even for a given PDOP value and a given number of satellites contacted. Finally, mobile collars performed less well than their static counterparts, presumably because of frequent changes of GPS position and orientation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".